Average Peak Signal-to-noise Ratio Research Articles

Ultrasound image denoising autoencoder model based on lightweight attention mechanism.

The presence of noise in medical ultrasound images significantly degrades image quality and affects the accuracy of disease diagnosis. The convolutional neural network-denoising autoencoder (CNN-DAE) model extracts feature information by stacking regularly sized kernels. This results in the loss of texture detail, the over-smoothing of the image, and a lack of generalizability for speckle noise. A lightweight attention denoise-convolutional neural network (LAD-CNN) is proposed in the present study. Two different lightweight attention blocks (i.e., the lightweight channel attention (LCA) block and the lightweight large-kernel attention (LLA) block are concatenated into the downsampling stage and the upsampling stage, respectively. A skip connection is included before the upsampling layer to alleviate the problem of gradient vanishing during backpropagation. The effectiveness of our model was evaluated using both subjective visual effects and objective evaluation metrics. With the highest peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) values at all noise levels, the proposed model outperformed the other models. In the test of brachial plexus ultrasound images, the average PSNR of our model was 0.15 higher at low noise levels and 0.33 higher at high noise levels than the suboptimal model. In the test of fetal ultrasound images, the average PSNR of our model was 0.23 higher at low noise levels and 0.20 higher at high noise levels than the suboptimal model. The statistical analysis showed that the p values were less than 0.05, which indicated a statistically significant difference between our model and the other models. The results of this study suggest that the proposed LAD-CNN model is more efficient in denoising and preserving image details than both conventional denoising algorithms and existing deep-learning algorithms.

Channel-wise attention enhanced and structural similarity constrained cycleGAN for effective synthetic CT generation from head and neck MRI images

BackgroundMagnetic resonance imaging (MRI) plays an increasingly important role in radiotherapy, enhancing the accuracy of target and organs at risk delineation, but the absence of electron density information limits its further clinical application. Therefore, the aim of this study is to develop and evaluate a novel unsupervised network (cycleSimulationGAN) for unpaired MR-to-CT synthesis.MethodsThe proposed cycleSimulationGAN in this work integrates contour consistency loss function and channel-wise attention mechanism to synthesize high-quality CT-like images. Specially, the proposed cycleSimulationGAN constrains the structural similarity between the synthetic and input images for better structural retention characteristics. Additionally, we propose to equip a novel channel-wise attention mechanism based on the traditional generator of GAN to enhance the feature representation capability of deep network and extract more effective features. The mean absolute error (MAE) of Hounsfield Units (HU), peak signal-to-noise ratio (PSNR), root-mean-square error (RMSE) and structural similarity index (SSIM) were calculated between synthetic CT (sCT) and ground truth (GT) CT images to quantify the overall sCT performance.ResultsOne hundred and sixty nasopharyngeal carcinoma (NPC) patients who underwent volumetric-modulated arc radiotherapy (VMAT) were enrolled in this study. The generated sCT of our method were more consistent with the GT compared with other methods in terms of visual inspection. The average MAE, RMSE, PSNR, and SSIM calculated over twenty patients were 61.88 ± 1.42, 116.85 ± 3.42, 36.23 ± 0.52 and 0.985 ± 0.002 for the proposed method. The four image quality assessment metrics were significantly improved by our approach compared to conventional cycleGAN, the proposed cycleSimulationGAN produces significantly better synthetic results except for SSIM in bone.ConclusionsWe developed a novel cycleSimulationGAN model that can effectively create sCT images, making them comparable to GT images, which could potentially benefit the MRI-based treatment planning.

Bio-Inspired Watermarking Method for Authentication of Fundus Images in Computer-Aided Diagnosis of Retinopathy

Nowadays, medical imaging has become an indispensable tool for the diagnosis of some pathologies and as a health prevention instrument. In addition, medical images are transmitted over all types of computer networks, many of them insecure or susceptible to intervention, making sensitive patient information vulnerable. Thus, image watermarking is a popular approach to embed copyright protection, Electronic Patient Information (EPR), institution information, or other digital image into medical images. However, in the medical field, the watermark must preserve the quality of the image for diagnosis purposes. In addition, the inserted watermark must be robust both to intentional and unintentional attacks, which try to delete or weaken it. This work presents a bio-inspired watermarking algorithm applied to retinal fundus images used in computer-aided retinopathy diagnosis. The proposed system uses the Steered Hermite Transform (SHT), an image model inspired by the Human Vision System (HVS), as a spread spectrum watermarking technique, by leveraging its bio-inspired nature to give imperceptibility to the watermark. In addition, the Singular Value Decomposition (SVD) is used to incorporate the robustness of the watermark against attacks. Moreover, the watermark is embedded into the RGB fundus images through the blood vessel patterns extracted by the SHT and using the luma band of Y’CbCr color model. Also, the watermark was encrypted using the Jigsaw Transform (JST) to incorporate an extra level of security. The proposed approach was tested using the image public dataset MESSIDOR-2, which contains 1748 8-bit color images of different sizes and presenting different Diabetic Retinopathy (DR). Thus, on the one hand, in the experiments we evaluate the proposed bio-inspired watermarking method over the entire MESSIDOR-2 dataset, showing that the embedding process does not affect the quality of the fundus images and the extracted watermark, by obtaining average Peak Signal-to-Noise Ratio (PSNR) values higher to 53 dB for the watermarked images and average PSNR values higher to 32 dB to the extracted watermark for the entire dataset. Also, we tested the method against image processing and geometric attacks successfully extracting the watermarking. A comparison of the proposed method against state-of-the-art was performed, obtaining competitive results. On the other hand, we classified the DR grade of the fundus image dataset using four trained deep learning models (VGG16, ResNet50, InceptionV3, and YOLOv8) to evaluate the inference results using the originals and marked images. Thus, the results show that DR grading remains both in the non-marked and marked images.

Radiation reduction for interventional radiology imaging: a video frame interpolation solution

PurposeThe aim of this study was to diminish radiation exposure in interventional radiology (IR) imaging while maintaining image quality. This was achieved by decreasing the acquisition frame rate and employing a deep neural network to interpolate the reduced frames.MethodsThis retrospective study involved the analysis of 1634 IR sequences from 167 pediatric patients (March 2014 to January 2022). The dataset underwent a random split into training and validation subsets (at a 9:1 ratio) for model training and evaluation. Our approach proficiently synthesized absent frames in simulated low-frame-rate sequences by excluding intermediate frames from the validation subset. Accuracy assessments encompassed both objective experiments and subjective evaluations conducted by nine radiologists.ResultsThe deep learning model adeptly interpolated the eliminated frames within IR sequences, demonstrating encouraging peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) results. The average PSNR values for angiographic, subtraction, and fluoroscopic modes were 44.94 dB, 34.84 dB, and 33.82 dB, respectively, while the corresponding SSIM values were 0.9840, 0.9194, and 0.7752. Subjective experiments conducted with experienced interventional radiologists revealed minimal discernible differences between interpolated and authentic sequences.ConclusionOur method, which interpolates low-frame-rate IR sequences, has shown the capability to produce high-quality IR images. Additionally, the model exhibits potential for reducing the frame rate during IR image acquisition, consequently mitigating radiation exposure.Critical relevance statementThis study presents a critical advancement in clinical radiology by demonstrating the effectiveness of a deep neural network in reducing radiation exposure during pediatric interventional radiology while maintaining image quality, offering a potential solution to enhance patient safety.Key points• Reducing radiation: cutting IR image to reduce radiation.• Accurate frame interpolation: our model effectively interpolates missing frames.• High visual quality in terms of PSNR and SSIM, making IR procedures safer without sacrificing quality.Graphical

High-resolution imaging in acoustic microscopy using deep learning

Acoustic microscopy is a cutting-edge label-free imaging technology that allows us to see the surface and interior structure of industrial and biological materials. The acoustic image is created by focusing high-frequency acoustic waves on the object and then detecting reflected signals. On the other hand, the quality of the acoustic image’s resolution is influenced by the signal-to-noise ratio, the scanning step size, and the frequency of the transducer. Deep learning-based high-resolution imaging in acoustic microscopy is proposed in this paper. To illustrate four times resolution improvement in acoustic images, five distinct models are used: SRGAN, ESRGAN, IMDN, DBPN-RES-MR64-3, and SwinIR. The trained model’s performance is assessed by calculating the PSNR (Peak Signal to Noise Ratio) and SSIM (Structural Similarity Index) between the network-predicted and ground truth images. To avoid the model from over-fitting, transfer learning was incorporated during the procedure. SwinIR had average SSIM and PSNR values of 0.95 and 35, respectively. The model was also evaluated using a biological sample from Reindeer Antler, yielding an SSIM score of 0.88 and a PSNR score of 32.93. Our framework is relevant to a wide range of industrial applications, including electronic production, material micro-structure analysis, and other biological applications in general.

An image inpainting-based data augmentation method for improved sclerosed glomerular identification performance with the segmentation model EfficientNetB3-Unet

The percent global glomerulosclerosis is a key factor in determining the outcome of renal transfer surgery. At present, the rate is typically computed by pathologists, which is labour intensive and nonstandardized. With the development of Deep Learning (DL), DL-based segmentation models can be used to better identify and segment normal and sclerosed glomeruli. Based on this, we can better quantify percent global glomerulosclerosis to reduce the discard rate of donor kidneys. We used 51 whole slide images (WSIs) from different institutions that are publicly available on the internet. However, the number of sclerosed glomeruli is much smaller than that of normal glomeruli in different WSIs, which can reduce the effectiveness of Deep Learning. For better sclerosed glomerular identification and segmentation performance, we modified and trained a GAN (generative adversarial network)-based image inpainting model to obtain more synthetic sclerosed glomeruli. Our proposed inpainting method achieved an average SSIM (Structural Similarity) of 0.8086 and an average PSNR (Peak Signal-to-Noise Ratio) of 22.8943 dB in the area of generated sclerosed glomeruli. We obtained sclerosed glomerular segmentation performance improvement by adding synthetic sclerosed glomerular images and achieved the best Dice of glomerular segmentation in different test sets based on the modified Unet model.

CDRSHNET: VARIANCE-GUIDED MULTISCALE AND SELF-ATTENTION FUSION WITH HYBRID LOSS FUNCTION TO RESTORE TRAFFIC-SIGN IMAGES CAPTURED IN ADVERSE CONDITIONS

This paper proposes a CDRSHNet (CodecDirtyRainyShadowHazeNetwork) architecture with a fusion of self-attention (SA) and variance-guided multiscale attention (VGMA) mechanism to restore traffic sign images captured in challenging weather conditions including raindrops, shadows, haze, blurry images from dirty camera lenses and codec errors. The SA captures global dependencies whereas VGMA enhances the representation by emphasizing informative channels and spatial locations. To enhance the image quality hybrid loss function is proposed that combines the Gradient Magnitude Similarity Deviation (GMSD) and Charbonnier loss. The CDRSHNet is trained on a dataset of real and synthesized images. Its performance is evaluated on the average Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR) on Test RID (Real Image Dataset) and Test SID (Synthesized Image Dataset). CDRSHNet achieved an average SSIM of 0.978 and an average PSNR of 39.58 on Test RID. On Test SID, the average SSIM is 0.963, and the average PSNR is 39.46

An adaptive image compression algorithm based on joint clustering algorithm and deep learning

AbstractIn recent years, deep artificial neural networks have attracted much attention and have been applied in various fields because they surpass the parameter fitting effect of traditional methods under the condition of data convergence. On the other hand, limited transmission bandwidth and storage capacity make image compression necessary in communication. Here, a compression algorithm that combines the K‐means clustering algorithm with the neural network algorithm is proposed. First, the pixel points of the image are clustered by K‐means algorithm in order to reduce the amount of data input to the neural network algorithm. Secondly, neural network is used to extract image features which realizes further compression. The experiment results show that the peak signal‐to‐noise ratio (PSNR) is 33.48 dB at most with compression ratio at 32:1. The ablation experiment shows that the run time speeds up 9.5% compared to the algorithm without K‐means clustering. Comprehensive comparison experiment shows that the average PSNR is 30.09 dB, which is larger than other baseline approaches. The proposed algorithm is an efficient solution for image compression.

Verifiable (t, n) Secret Image Sharing Scheme Based on Slim Turtle Shell Matrix

As Internet technology advances rapidly, safeguarding confidential messages has become an increasingly pressing concern. Among the various schemes available, secret image sharing (SIS) has gained considerable attention. Particularly, the (t, n) secret sharing scheme offers robust protection for hidden secret messages. However, it remains challenging to expand the quality of t and n while maintaining acceptable image quality for the share images and enhancing security features. To address these challenges, we have developed a variant turtle shell known as the slim turtle shell, which we incorporate into our innovative (t, n)-SIS scheme. The slim turtle shell technique not only facilitates the creation of meaningful shares but also ensures that the resulting meaningful shares maintain a high level of visual quality, thanks to its impressive embedding capabilities. Additionally, we have devised two authentication mechanisms to validate the integrity of both shares and secret images. These mechanisms facilitate the detection of tampering during the secret message extraction and verification phase, should the integrity be compromised. Empirical results from our experiments demonstrate an impressive embedding capacity of 1,572,864 bits in our proposed scheme, coupled with an average PSNR (Peak Signal-to-Noise Ratio) of 45.80 dB. Furthermore, our scheme demonstrates superior authentication capabilities when compared to other existing schemes.

Invisible Shield: Unveiling an Efficient Watermarking Solution for Medical Imaging Security

Securing medical imaging poses a significant challenge in preserving the confidentiality of healthcare data. Numerous research efforts have focused on fortifying these images, with encryption emerging as a primary solution for maintaining data integrity without compromising confidentiality. However, applying conventional encryption techniques directly to e-health data encounters hurdles, including limitations in data size, redundancy, and capacity, particularly in open-channel patient data transmissions. As a result, the unique characteristics of images, marked by their risk of data loss and the need for confidentiality, make preserving the privacy of data contents a complex task. This underscores the pressing need for innovative approaches to ensure the security and confidentiality of sensitive healthcare information within medical images. The proposed algorithm outperforms referenced algorithms in both image fidelity and steganographic capacity across diverse medical imaging modalities. It consistently achieves higher Peak Signal-to-Noise Ratio (PSNR) values, indicating superior image fidelity, reduced noise, and preserved signal quality in CT, MRI, ultrasound, and X-ray modalities. The experimental results demonstrate a considerable improvement in both the Peak Signal-to-Noise Ratio (PSNR) and maximum embedding capacity. Specifically, the average PSNR value for the X-ray modality reached a notable 73 dB, signifying superior image quality. Moreover, the CT modality exhibited the highest maximum embedding capacity, measured at 0.52, showcasing its efficiency in accommodating data within the images. Moreover, the algorithm consistently offers increased steganographic data hiding capacity in these images without perceptibly degrading their quality or integrity.

Efficient estimation of pharmacokinetic parameters from breast dynamic contrast-enhanced MRI based on a convolutional neural network for predicting molecular subtypes

Objective. Tracer kinetic models allow for estimating pharmacokinetic (PK) parameters, which are related to pathological characteristics, from breast dynamic contrast-enhanced magnetic resonance imaging. However, existing tracer kinetic models subject to inaccuracy are time-consuming for PK parameters estimation. This study aimed to accurately and efficiently estimate PK parameters for predicting molecular subtypes based on convolutional neural network (CNN). Approach. A CNN integrating global and local features (GL-CNN) was trained using synthetic data where known PK parameters map was used as the ground truth, and subsequently used to directly estimate PK parameters (volume transfer constant K trans and flux rate constant K ep) map. The accuracy assessed by the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and concordance correlation coefficient (CCC) was compared between the GL-CNN and Tofts-based PK parameters in synthetic data. Radiomic features were calculated from the PK parameters map in 208 breast tumors. A random forest classifier was constructed to predict molecular subtypes using a discovery cohort (n = 144). The diagnostic performance evaluated on a validation cohort (n = 64) using the area under the receiver operating characteristic curve (AUC) was compared between the GL-CNN and Tofts-based PK parameters. Main results. The average PSNR (48.8884), SSIM (0.9995), and CCC (0.9995) between the GL-CNN-based K trans map and ground truth were significantly higher than those between the Tofts-based K trans map and ground truth. The GL-CNN-based K trans obtained significantly better diagnostic performance (AUCs = 0.7658 and 0.8528) than the Tofts-based K trans for luminal B and HER2 tumors. The GL-CNN method accelerated the computation by speed approximately 79 times compared to the Tofts method for the whole breast of all patients. Significance. Our results indicate that the GL-CNN method can be used to accurately and efficiently estimate PK parameters for predicting molecular subtypes.

Synthetic 3D Spinal Vertebrae Reconstruction from Biplanar X-rays Utilizing Generative Adversarial Networks.

Computed tomography (CT) offers detailed insights into the internal anatomy of patients, particularly for spinal vertebrae examination. However, CT scans are associated with higher radiation exposure and cost compared to conventional X-ray imaging. In this study, we applied a Generative Adversarial Network (GAN) framework to reconstruct 3D spinal vertebrae structures from synthetic biplanar X-ray images, specifically focusing on anterior and lateral views. The synthetic X-ray images were generated using the DRRGenerator module in 3D Slicer by incorporating segmentations of spinal vertebrae in CT scans for the region of interest. This approach leverages a novel feature fusion technique based on X2CT-GAN to combine information from both views and employs a combination of mean squared error (MSE) loss and adversarial loss to train the generator, resulting in high-quality synthetic 3D spinal vertebrae CTs. A total of n = 440 CT data were processed. We evaluated the performance of our model using multiple metrics, including mean absolute error (MAE) (for each slice of the 3D volume (MAE0) and for the entire 3D volume (MAE)), cosine similarity, peak signal-to-noise ratio (PSNR), 3D peak signal-to-noise ratio (PSNR-3D), and structural similarity index (SSIM). The average PSNR was 28.394 dB, PSNR-3D was 27.432, SSIM was 0.468, cosine similarity was 0.484, MAE0 was 0.034, and MAE was 85.359. The results demonstrated the effectiveness of this approach in reconstructing 3D spinal vertebrae structures from biplanar X-rays, although some limitations in accurately capturing the fine bone structures and maintaining the precise morphology of the vertebrae were present. This technique has the potential to enhance the diagnostic capabilities of low-cost X-ray machines while reducing radiation exposure and cost associated with CT scans, paving the way for future applications in spinal imaging and diagnosis.

Image reconstruction using UNET-transformer network for fast and low-dose PET scans

IntroductionLow-dose and fast PET imaging (low-count PET) play a significant role in enhancing patient safety, healthcare efficiency, and patient comfort during medical imaging procedures. To achieve high-quality images with low-count PET scans, effective reconstruction models are crucial for denoising and enhancing image quality. The main goal of this paper is to develop an effective and accurate deep learning-based method for reconstructing low-count PET images, which is a challenging problem due to the limited amount of available data and the high level of noise in the acquired images. The proposed method aims to improve the quality of reconstructed PET images while preserving important features, such as edges and small details, by combining the strengths of UNET and Transformer networks. Material and methodsThe proposed TrUNET-MAPEM model integrates a residual UNET-transformer regularizer into the unrolled maximum a posteriori expectation maximization (MAPEM) algorithm for PET image reconstruction. A loss function based on a combination of structural similarity index (SSIM) and mean squared error (MSE) is utilized to evaluate the accuracy of the reconstructed images. The simulated dataset was generated using the Brainweb phantom, while the real patient dataset was acquired using a Siemens Biograph mMR PET scanner. We also implemented state-of-the-art methods for comparison purposes: OSEM, MAPOSEM, and supervised learning using 3D-UNET network. The reconstructed images are compared to ground truth images using metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and relative root mean square error (rRMSE) to quantitatively evaluate the accuracy of the reconstructed images. ResultsOur proposed TrUNET-MAPEM approach was evaluated using both simulated and real patient data. For the patient data, our model achieved an average PSNR of 33.72 dB, an average SSIM of 0.955, and an average rRMSE of 0.39. These results outperformed other methods which had average PSNRs of 36.89 dB, 34.12 dB, and 33.52 db, average SSIMs of 0.944, 0.947, and 0.951, and average rRMSEs of 0.59, 0.49, and 0.42. For the simulated data, our model achieved an average PSNR of 31.23 dB, an average SSIM of 0.95, and an average rRMSE of 0.55. These results also outperformed other state-of-the-art methods, such as OSEM, MAPOSEM, and 3DUNET-MAPEM. The model demonstrates the potential for clinical use by successfully reconstructing smooth images while preserving edges. The comparison with other methods demonstrates the superiority of our approach, as it outperforms all other methods for all three metrics. ConclusionThe proposed TrUNET-MAPEM model presents a significant advancement in the field of low-count PET image reconstruction. The results demonstrate the potential for clinical use, as the model can produce images with reduced noise levels and better edge preservation compared to other reconstruction and post-processing algorithms. The proposed approach may have important clinical applications in the early detection and diagnosis of various diseases.

RCA-GAN: An Improved Image Denoising Algorithm Based on Generative Adversarial Networks

Image denoising, as an essential component of image pre-processing, effectively reduces noise interference to enhance image quality, a factor of considerable research importance. Traditional denoising methods often lead to the blurring of image details and a lack of realism at the image edges. To deal with these issues, we propose an image denoising algorithm named Residual structure and Cooperative Attention mechanism based on Generative Adversarial Networks (RCA-GAN). This algorithm proficiently reduces noise while focusing on preserving image texture details. To maximize feature extraction, this model first employs residual learning within a portion of the generator’s backbone, conducting extensive multi-dimensional feature extraction to preserve a greater amount of image details. Secondly, it introduces a simple yet efficient cooperative attention module to enhance the representation capacity of edge and texture features, further enhancing the preservation of intricate image details. Finally, this paper constructs a novel loss function—the Multimodal Loss Function—for the network training process. The experimental results were evaluated using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) as evaluation metrics. The experimental results demonstrate that the proposed RCA-GAN image denoising algorithm has increased the average PSNR from 24.71 dB to 33.76 dB, achieving a 36.6% improvement. Additionally, the average SSIM value has risen from 0.8451 to 0.9503, indicating a 12.4% enhancement. It achieves superior visual outcomes, showcasing the ability to preserve image texture details to a greater extent and excel in edge preservation and noise suppression.

An Image Denoising Technique Using Wavelet-Anisotropic Gaussian Filter-Based Denoising Convolutional Neural Network for CT Images

Denoising computed tomography (CT) medical images is crucial in preserving information and restoring images contaminated with noise. Standard filters have extensively been used for noise removal and fine details’ preservation. During the transmission of medical images, noise degrades the visibility of anatomical structures and subtle abnormalities, making it difficult for radiologists to accurately diagnose and interpret medical conditions. In recent studies, an optimum denoising filter using the wavelet threshold and deep-CNN was used to eliminate Gaussian noise in CT images using the image quality index (IQI) and peak signal-to-noise ratio (PSNR). Although the results were better than those with traditional techniques, the performance resulted in a loss of clarity and fine details’ preservation that rendered the CT images unsuitable. To address these challenges, this paper focuses on eliminating noise in CT scan images corrupted with additive Gaussian blur noise (AGBN) using an ensemble approach that integrates anisotropic Gaussian filter (AGF) and wavelet transform with a deep learning denoising convolutional neural network (DnCNN). First, the noisy image is denoised by AGF and Haar wavelet transform as preprocessing operations to eliminate AGBN. The DnCNN is then combined with AGF and wavelet for post-processing operation to eliminate the rest of the noises. Specifically, we used AGF due to its adaptability to edge orientation and directional information, which prevents blurring along edges for non-uniform noise distribution. Denoised images are evaluated using PSNR, mean squared error (MSE), and the structural similarity index measure (SSIM). Results revealed that the average PSNR value of the proposed ensemble approach is 28.28, and the average computational time is 0.01666 s. The implication is that the MSE between the original and reconstructed images is very low, implying that the image is restored correctly. Since the SSIM values are between 0 and 1.0, 1.0 perfectly matches the reconstructed image with the original image. In addition, the SSIM values at 1.0 or near 1.0 implicitly reveal a remarkable structural similarity between the denoised CT image and the original image. Compared to other techniques, the proposed ensemble approach has demonstrated exceptional performance in maintaining the quality of the image and fine details’ preservation.

Probabilistic Modeling of Multicamera Interference for Time-of-Flight Sensors.

The behavior of multicamera interference in 3D images (e.g., depth maps), which is based on infrared (IR) light, is not well understood. In 3D images, when multicamera interference is present, there is an increase in the amount of zero-value pixels, resulting in a loss of depth information. In this work, we demonstrate a framework for synthetically generating direct and indirect multicamera interference using a combination of a probabilistic model and ray tracing. Our mathematical model predicts the locations and probabilities of zero-value pixels in depth maps that contain multicamera interference. Our model accurately predicts where depth information may be lost in a depth map when multicamera interference is present. We compare the proposed synthetic 3D interference images with controlled 3D interference images captured in our laboratory. The proposed framework achieves an average root mean square error (RMSE) of 0.0625, an average peak signal-to-noise ratio (PSNR) of 24.1277 dB, and an average structural similarity index measure (SSIM) of 0.9007 for predicting direct multicamera interference, and an average RMSE of 0.0312, an average PSNR of 26.2280 dB, and an average SSIM of 0.9064 for predicting indirect multicamera interference. The proposed framework can be used to develop and test interference mitigation techniques that will be crucial for the successful proliferation of these devices.

Cooperative Swarm Intelligence Algorithms for Adaptive Multilevel Thresholding Segmentation of COVID-19 CT-Scan Images

The Coronavirus Disease 2019 (COVID-19) is widespread throughout the world and poses a serious threat to public health and safety. A COVID-19 infection can be recognized using computed tomography (CT) scans. To enhance the categorization, some image segmentation techniques are presented to extract regions of interest from COVID-19 CT images. Multi-level thresholding (MLT) is one of the simplest and most effective image segmentation approaches, especially for grayscale images like CT scan images. Traditional image segmentation methods use histogram approaches; however, these approaches encounter some limitations. Now, swarm intelligence inspired meta-heuristic algorithms have been applied to resolve MLT, deemed an NP-hard optimization task. Despite the advantages of using meta-heuristics to solve global optimization tasks, each approach has its own drawbacks. However, the common flaw for most meta-heuristic algorithms is that they are unable to maintain the diversity of their population during the search, which means they might not always converge to the global optimum. This study proposes a cooperative swarm intelligence-based MLT image segmentation approach that hybridizes the advantages of parallel meta-heuristics and MLT for developing an efficient image segmentation method for COVID-19 CT images. An efficient cooperative model-based meta-heuristic called the CPGH is developed based on three practical algorithms: particle swarm optimization (PSO), grey wolf optimizer (GWO), and Harris hawks optimization (HHO). In the cooperative model, the applied algorithms are executed concurrently, and a number of potential solutions are moved across their populations through a procedure called migration after a set number of generations. The CPGH model can solve the image segmentation problem using MLT image segmentation. The proposed CPGH is evaluated using three objective functions, cross-entropy, Otsu’s, and Tsallis, over the COVID-19 CT images selected from open-sourced datasets. Various evaluation metrics covering peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and universal quality image index (UQI) were employed to quantify the segmentation quality. The overall ranking results of the segmentation quality metrics indicate that the performance of the proposed CPGH is better than conventional PSO, GWO, and HHO algorithms and other state-of-the-art methods for MLT image segmentation. On the tested COVID-19 CT images, the CPGH offered an average PSNR of 24.8062, SSIM of 0.8818, and UQI of 0.9097 using 20 thresholds.

Diffraction model-driven neural network trained using hybrid domain loss for real-time and high-quality computer-generated holography.

Learning-based computer-generated holography (CGH) has demonstrated great potential in enabling real-time, high-quality holographic displays. However, most existing learning-based algorithms still struggle to produce high-quality holograms, due to the difficulty of convolutional neural networks (CNNs) in learning cross-domain tasks. Here, we present a diffraction model-driven neural network (Res-Holo) using hybrid domain loss for phase-only hologram (POH) generation. Res-Holo utilizes the weights of the pretrained ResNet34 as the initialization during the encoder stage of the initial phase prediction network to extract more generic features and also to help prevent overfitting. Also, frequency domain loss is added to further constrain the information that the spatial domain loss is insensitive. The peak signal-to-noise ratio (PSNR) of the reconstructed image is improved by 6.05 dB using hybrid domain loss compared to using spatial domain loss alone. Simulation results show that the proposed Res-Holo can generate high-fidelity 2 K resolution POHs with an average PSNR of 32.88 dB at 0.014 seconds/frame on the DIV2K validation set. Both monochrome and full-color optical experiments show that the proposed method can effectively improve the quality of reproduced images and suppress image artifacts.

Adaptive fusion of multi-exposure images based on perceptron model

Abstract Multi-exposure image fusion as a technical means to bridge the dynamic range gap between real scenes and image acquisition devices, which makes the fused images better quality and more realistic and vivid simulation of real scenes, has been widely concerned by scholars from various countries. In order to improve the adaptive fusion effect of multi-exposure images, this paper proposes a fusion algorithm based on multilayer perceptron (MLP) based on the perceptron model and verifies the feasibility of the algorithm by the peak signal-to-noise ratio (PSNR), correlation coefficient (PCC), structural similarity (SSMI) and HDR-VDR-2, an evaluation index of HDR image quality. Comparison with other algorithms revealed that the average PSNR of the MLP algorithm improved by 4.43% over the Ma algorithm, 7.88% over the Vanmail algorithm, 10.30% over the FMMR algorithm, 11.19% over the PMF algorithm, and 11.19% over the PMF algorithm. For PCC, the MLP algorithm improves by 20.14%, 17.46%, 2.31%, 11.24%, and 15.36% over the other algorithms in that order. For SSMI, the MLP algorithm improved by 16.99%, 8.96%, 17.17%, 14.41%, and 4.85% over the other algorithms, in that order. For HDR-VDR-2, the MLP algorithm improved by 3.02%, 2.79%, 6.84%, 4.90%, and 6.55% over the other algorithms, in that order. The results show that the MLP algorithm can avoid image artifacts while retaining more details. The MLP-based adaptive fusion method is a step further in the theoretical study of multi-exposure image fusion, which is of great significance for subsequent research and practical application by related technology vendors.

An improved defocusing adaptive style transfer method based on a stroke pyramid.

Image style transfer aims to assign a specified artist's style to a real image. However, most existing methods cannot generate textures of various thicknesses due to the rich semantic information of the input image. The image loses some semantic information through style transfer with a uniform stroke size. To address the above problems, we propose an improved multi-stroke defocus adaptive style transfer framework based on a stroke pyramid, which mainly fuses various stroke sizes in the image spatial dimension to enhance the image content interpretability. We expand the receptive field of each branch and then fuse the features generated by the multiple branches based on defocus degree. Finally, we add an additional loss term to enhance the structural features of the generated image. The proposed model is trained using the Common Objects in Context (COCO) and Synthetic Depth of Field (SYNDOF) datasets, and the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) are used to evaluate the overall quality of the output image and its structural similarity with the content image, respectively. To validate the feasibility of the proposed algorithm, we compare the average PSNR and SSIM values of the output of the modified model and those of the original model. The experimental results show that the modified model improves the PSNR and SSIM values of the outputs by 1.43 and 0.12 on average, respectively. Compared with the single-stroke style transfer method, the framework proposed in this study improves the readability of the output images with more abundant visual expression.