PSNR Research Articles

Image Multi-threshold Segmentation Based on an Ameliorated Harmony Search Optimization Algorithm

Image segmentation is the basis and premise of image processing, though traditional multi-threshold image segmentation methods are simple and effective, they suffer the problems of low accuracy and slow convergence rate. For that reason, this paper introduces the multi-threshold image segmentation scheme by combining the harmony search (HS) optimization algorithm and the maximum between-class variance (Otsu) to solve them. Firstly, to further improve the performance of the basic HS, an ameliorated harmony search (AHS) is put forward by modifying the generation method of the new harmony improvisation and introducing a convergence coefficient. Secondly, the AHS algorithm, which takes the maximum between-class variance as its objective function, namely AHS-Otsu, is applied to image multi-level threshold segmentation. Finally, six test images are selected to verify the multilevel segmentation performance of AHS-Otsu. Peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) are two commonly used metrics for evaluating the effectiveness of image segmentation, which are both used in this article. Comprehensive experimental results indicate that the AHS-Otsu does not only has fast segmentation processing speed, but also can obtain more accurate segmentation performance than others, which prove the effectiveness and potential of the AHS-Otsu algorithm in the field of image segmentation especially for the multi-threshold.

Restoration of motion-blurred numeral image using a complex-amplitude diffractive processor.

We propose a complex-amplitude diffractive processor based on diffractive deep neural networks (D2NNs). By precisely controlling the propagation of an optical field, it can effectively remove the motion blur in numeral images and realize the restoration. Comparative analysis of phase-only, amplitude-only, and complex-amplitude diffractive processor reveals that the complex-amplitude network significantly enhances the performance of the processor and improves the peak signal-to-noise ratio (PSNR) of the images. Appropriate use of complex-amplitude networks contributes to reduce the number of network layers and alleviates alignment difficulties. Due to its fast processing speed and low power consumption, complex-amplitude diffractive processors hold potential applications in various fields including road monitoring, sports photography, satellite imaging, and medical diagnostics.

Frequency-Aware Divide-and-Conquer for Efficient Real Noise Removal.

Deep-learning-based approaches have achieved remarkable progress for complex real scenario denoising, yet their accuracy-efficiency tradeoff is still understudied, particularly critical for mobile devices. As real noise is unevenly distributed relative to underlay signals in different frequency bands, we introduce a frequency-aware divide-and-conquer strategy to develop a frequency-aware denoising network (FADN). FADN is materialized by stacking frequency-aware denoising blocks (FADBs), in which a denoised image is progressively predicted by a series of frequency-aware noise dividing and conquering operations. For noise dividing, FADBs decompose the noisy and clean image pairs into low-and high-frequency representations via a wavelet transform (WT) followed by an invertible network and recover the final denoised image by integrating the denoised information from different frequency bands. For noise conquering, the separated low-frequency representation of the noisy image is kept as clean as possible by the supervision of the clean counterpart, while the high-frequency representation combining the estimated residual from the successive FADB is purified under the corresponding accompanied supervision for residual compensation. Since our FADN progressively and pertinently denoises from frequency bands, the accuracy-efficiency tradeoff can be controlled as a requirement by the number of FADBs. Experimental results on the SIDD, DND, and NAM datasets show that our FADN outperforms the state-of-the-art methods by improving the peak signal-to-noise ratio (PSNR) and decreasing the model parameters. The code is released at https://github.com/NekoDaiSiki/FADN.

A novel image semantic communication method via dynamic decision generation network and generative adversarial network

Effectively compressing transmitted images and reducing the distortion of reconstructed images are challenges in image semantic communication. This paper proposes a novel image semantic communication model that integrates a dynamic decision generation network and a generative adversarial network to address these challenges as efficiently as possible. At the transmitter, features are extracted and selected based on the channel’s signal-to-noise ratio (SNR) using semantic encoding and a dynamic decision generation network. This semantic approach can effectively compress transmitted images, thereby reducing communication traffic. At the receiver, the generator/decoder collaborates with the discriminator network, enhancing image reconstruction quality through adversarial and perceptual losses. The experimental results on the CIFAR-10 dataset demonstrate that our scheme achieves a peak SNR of 26 dB, a structural similarity of 0.9, and a compression ratio (CR) of 81.5% in an AWGN channel with an SNR of 3 dB. Similarly, in the Rayleigh fading channel, the peak SNR is 23 dB, structural similarity is 0.8, and the CR is 80.5%. The learned perceptual image patch similarity in both channels is below 0.008. These experiments thoroughly demonstrate that the proposed semantic communication is a superior deep learning-based joint source-channel coding method, offering a high CR and low distortion of reconstructed images.

A high-quality self-supervised image denoising method based on SDDW-GAN and CHRNet

Image denoising remains a classic and crucial issue in the field of image processing, significantly impacting the outcomes of subsequent image processing tasks. For instance, the denoising network depends on “noise-clean” image pairs to train network effectively. However, it is often hampered by issues such as useful information loss, low training efficiency, and poor blind denoising. To address these challenges, this study proposes a novel image denoising network that integrates the complementary strengths of model-based and learning-based approaches, specifically leveraging the capabilities of both SDDW-GAN and CHRNet. Firstly, SDDW-GAN is designed to estimate the noise distribution on the input noisy images, and a fast-smoothing noisy block sampling algorithm is proposed to extract the noise blocks in noisy images in SDDW-GAN. Secondly, a network with dual generators and dual discriminators based on W-GAN is designed to estimate the noise distribution on the input noisy images and generate noise sample pairs with the same noise distribution, which solves the problem of relying on “noise-clean” image pairs. Thirdly, CHRNet is designed to compute the mapping relationship between the double-noise samples and the single-noise samples. In order to further improve the denoising effect, the dual-channel residual attention module is proposed for fusion learning of global and local features. Experimental results show that the proposed method has a better denoising effect in complex environments and outperforms existing denoising methods. Specifically, in comparison with the stand-alone denoising methods BM3D, DnCNN, Noise2Noise, and Blind2Unblind, the proposed method improves the average peak signal-to-noise ratio (PSNR) by 0.23 dB to 0.78 dB on two benchmarking datasets crossing different noise levels. Its denoising effect is also greater than other competitive stand-alone and combination methods. The proposed method can also extend to low-light image enhancement, deblurring, and super-resolution.

Improved protection for colour images via invertible colour‐to‐grey, image distortion, and visible‐imperceptible watermarking

AbstractInvertible colour‐to‐grey algorithms protect the colour of an image against illegal usage and distribution. These schemes hide the colour information into a grayscale version of the image, making colour recovery without proper authorization challenging. A previous study proposed the incorporation of content security by distorting the protected image to hinder illegal structure restoration. This work addresses the limitations of this method, significantly improving both processing time and the quality of the recovered colour image. Specifically, processing times are reduced from 45 h to 9 s, and the mean PSNR value is increased by up to 2.23 dB. Furthermore, this proposal achieves competitive results compared to existing approaches that do not implement content security, obtaining mean peak signal‐to‐noise ratio (PSNR) and structural similarity index measure (SSIM) values above 42.32 dB and 0.992, respectively. Finally, quantitative results are supported by visual comparisons.

Generative adversarial networks with deep blind degradation powered terahertz ptychography

Ptychography is an imaging technique that uses the redundancy of information generated by the overlapping of adjacent light regions to calculate the relative phase of adjacent regions and reconstruct the image. In the terahertz domain, in order to make the ptychography technology better serve engineering applications, we propose a set of deep learning terahertz ptychography system that is easier to realize in engineering and plays an outstanding role. To address this issue, we propose to use a powerful deep blind degradation model which uses isotropic and anisotropic Gaussian kernels for random blurring, chooses the downsampling modes from nearest interpolation, bilinear interpolation, bicubic interpolation and down-up-sampling method, and introduces Gaussian noise, JPEG compression noise, and processed detector noise. Additionally, a random shuffle strategy is used to further expand the degradation space of the image. Using paired low/high resolution images generated by the deep blind degradation model, we trained a multi-layer residual network with residual scaling parameters and dense connection structure to achieve the neural network super-resolution of terahertz ptychography for the first time. We use two representative neural networks, SwinIR and RealESRGAN, to compare with our model. Experimental result shows that the proposed method achieved better accuracy and visual improvement than other terahertz ptychographic image super-resolution algorithms. Further quantitative calculation proved that our method has significant advantages in terahertz ptychographic image super-resolution, achieving a resolution of 33.09 dB on the peak signal-to-noise ratio (PSNR) index and 3.05 on the naturalness image quality estimator (NIQE) index. This efficient and engineered approach fills the gap in the improvement of terahertz ptychography by using neural networks.

Learning-Based Super-Resolution Imaging of Turbulent Flames in Both Time and 3D Space Using Double GAN Architectures

This article presents a spatiotemporal super-resolution (SR) reconstruction model for two common flame types, a swirling and then a jet flame, using double generative adversarial network (GAN) architectures. The approach develops two sets of generator and discriminator networks to learn topographic and temporal features and infer high spatiotemporal resolution turbulent flame structure from supplied low-resolution counterparts at two time points. In this work, numerically simulated 3D turbulent swirling and jet flame structures were used as training data to update the model parameters of the GAN networks. The effectiveness of our model was then thoroughly evaluated in comparison to other traditional interpolation methods. An upscaling factor of 2 in space, which corresponded to an 8-fold increase in the total voxel number and a double time frame acceleration, was used to verify the model’s ability on a swirling flame. The results demonstrate that the assessment metrics, peak signal-to-noise ratio (PSNR), overall error (ER), and structural similarity index (SSIM), with average values of 35.27 dB, 1.7%, and 0.985, respectively, in the spatiotemporal SR results, can reach acceptable accuracy. As a second verification to highlight the present model’s potential universal applicability to flame data of diverse types and shapes, we applied the model to a turbulent jet flame and had equal success. This work provides a different method for acquiring high-resolution 3D structure and further boosting repeat rate, demonstrating the potential of deep learning technology for combustion diagnosis.

DAW-FA: Domain-aware adaptive weighting with fine-grain attention for unsupervised MRI harmonization

Magnetic resonance (MR) imaging often lacks standardized acquisition protocols across various sites, leading to contrast variations that reduce image quality and hinder automated analysis. MR harmonization improves consistency by integrating data from multiple sources, ensuring reproducible analysis. Recent advances leverage image-to-image translation and disentangled representation learning to decompose anatomical and contrast representations, achieving consistent cross-site harmonization. However, these methods face two significant drawbacks: imbalanced contrast availability during training affects adaptation performance, and insufficient utilization of spatial variability in local anatomical structures limits model adaptability to different sites. To address these challenges, we propose Domain-aware Adaptive Weighting with Fine-Grain Attention (DAW-FA) for Unsupervised MRI Harmonization. DAW-FA incorporates an adaptive weighting mechanism and enhanced self-attention to mitigate MR contrast imbalance during training and account for spatial variability in local anatomical structures. This facilitates robust cross-site harmonization without requiring paired inter-site images. We evaluated DAW-FA on MR datasets with varying scanners and acquisition protocols. Experimental results show DAW-FA outperforms existing methods, with an average increase of 1.92 ± 0.56 in Peak Signal-to-Noise Ratio (PSNR) and 0.023 ± 0.011 in Structural Similarity Index Measure (SSIM). Additionally, we demonstrate DAW-FA’s impact on downstream tasks: Alzheimer’s disease classification and whole-brain segmentation, highlighting its potential clinical relevance.

Enhanced IDOL segmentation framework using personalized hyperspace learning IDOL.

Adaptive radiotherapy (ART) workflows have been increasingly adopted to achieve dose escalation and tissue sparing under shifting anatomic conditions, but the necessity of recontouring and the associated time burden hinders a real-time or online ART workflow. In response to this challenge, approaches to auto-segmentation involving deformable image registration, atlas-based segmentation, and deep learning-based segmentation (DLS) have been developed. Despite the particular promise shown by DLS methods, implementing these approaches in a clinical setting remains a challenge, namely due to the difficulty of curating a data set of sufficient size and quality so as to achieve generalizability in a trained model. To address this challenge, we have developed an intentional deep overfit learning (IDOL) framework tailored to the auto-segmentation task. However, certain limitations were identified, particularly the insufficiency of the personalized dataset to effectively overfit the model. In this study, we introduce a personalized hyperspace learning (PHL)-IDOL segmentation framework capable of generating datasets that induce the model to overfit specific patient characteristics for medical image segmentation. The PHL-IDOL model is trained in two stages. In the first, a conventional, general model is trained with a diverse set of patient data (n=100 patients) consisting of CT images and clinical contours. Following this, the general model is tuned with a data set consisting of two components: (a) selection of a subset of the patient data (m<n) using the similarity metrics (mean square error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and the universal quality image index (UQI) values); (b) adjust the CT and the clinical contours using a deformed vector generated from the reference patient and the selected patients using (a). After training, the general model, the continual model, the conventional IDOL model, and the proposed PHL-IDOL model were evaluated using the volumetric dice similarity coefficient (VDSC) and the Hausdorff distance 95% (HD95%) computed for 18 structures in 20 test patients. Implementing the PHL-IDOL framework resulted in improved segmentation performance for each patient. The Dice scores increased from 0.81 0.05 with the general model, 0.83 for the continual model, 0.83 for the conventional IDOL model to an average of 0.87 with the PHL-IDOL model. Similarly, the Hausdorff distance decreased from 3.06 with the general model, 2.84 for the continual model, 2.79 for the conventional IDOL model and 2.36 for the PHL-IDOL model. All the standard deviations were decreased by nearly half of the values comparing the general model and the PHL-IDOL model. The PHL-IDOL framework applied to the auto-segmentation task achieves improved performance compared to the general DLS approach, demonstrating the promise of leveraging patient-specific prior information in a task central to online ART workflows.

محاكاة مخططات التصفية المطبقة لتحسين الصور الفوتوغرافية

In signal processing, filters generally play a pivotal role to remove unwanted parts of the signal, such as random noise, or to extract useful parts of the signal, such as the components lying within a certain frequency range to enhance the performance and denoising scenario. This paper presents the implementation of linear and non-linear filtering schemes for noise reduction and images enhancement. This is achieved by performing convolution of a grayscale image with a mask filter of multiple sizes using MATLAB software. The average and median filters are implemented on the same image of an injected salt and pepper noise. In accordance with the findings, the nonlinear filters show more promising performance with a better peak-signal-noise ratio (PSNR) compared to the linear filters.

Self-normalization for a 1 mm3 resolution clinical PET system using deep learning

Objective. This work proposes, for the first time, an image-based end-to-end self-normalization framework for positron emission tomography (PET) using conditional generative adversarial networks (cGANs). Approach. We evaluated different approaches by exploring each of the following three methodologies. First, we used images that were either unnormalized or corrected for geometric factors, which encompass all time-invariant factors, as input data types. Second, we set the input tensor shape as either a single axial slice (2D) or three contiguous axial slices (2.5D). Third, we chose either Pix2Pix or polarized self-attention (PSA) Pix2Pix, which we developed for this work, as a deep learning network. The targets for all approaches were the axial slices of images normalized using the direct normalization method. We performed Monte Carlo simulations of ten voxelized phantoms with the SimSET simulation tool and produced 26,000 pairs of axial image slices for training and testing. Main results. The results showed that 2.5D PSA Pix2Pix trained with geometric-factors-corrected input images achieved the best performance among all the methods we tested. All approaches improved general image quality figures of merit peak signal to noise ratio (PSNR) and structural similarity index (SSIM) from ∼15 % to ∼55 %, and 2.5D PSA Pix2Pix showed the highest PSNR (28.074) and SSIM (0.921). Lesion detectability, measured with region of interest (ROI) PSNR, SSIM, normalized contrast recovery coefficient, and contrast-to-noise ratio, was generally improved for all approaches, and 2.5D PSA Pix2Pix trained with geometric-factors-corrected input images achieved the highest ROI PSNR (28.920) and SSIM (0.973). Significance. This study demonstrates the potential of an image-based end-to-end self-normalization framework using cGANs for improving PET image quality and lesion detectability without the need for separate normalization scans.

Deep Learning for Point-of-Care Ultrasound Image Quality Enhancement: A Review

The popularity of handheld devices for point-of-care ultrasound (POCUS) has increased in recent years due to their portability and cost-effectiveness. However, POCUS has the drawback of lower imaging quality compared to conventional ultrasound because of hardware limitations. Improving the quality of POCUS through post-image processing would therefore be beneficial, with deep learning approaches showing promise in this regard. This review investigates the state-of-the-art progress of image enhancement using deep learning suitable for POCUS applications. A systematic search was conducted from January 2024 to February 2024 on PubMed and Scopus. From the 457 articles that were found, the full text was retrieved for 69 articles. From this selection, 15 articles were identified addressing multiple quality enhancement aspects. A disparity in the baseline performance of the low-quality input images was seen across these studies, ranging between 8.65 and 29.24 dB for the Peak Signal-to-Noise Ratio (PSNR) and between 0.03 an 0.71 for the Structural Similarity Index Measure (SSIM). In six studies, where both the PSNR and the SSIM metrics were reported for the baseline and the generated images, mean differences of 6.60 (SD ± 2.99) and 0.28 (SD ± 0.15) were observed for the PSNR and SSIM, respectively. The reported performance outcomes demonstrate the potential of deep learning-based image enhancement for POCUS. However, variability in the extent of the performance gain across datasets and articles was notable, and the heterogeneity across articles makes quantifying the exact improvements challenging.

Medical image segmentation approach based on hybrid adaptive differential evolution and crayfish optimizer

Image segmentation plays a pivotal role in medical image analysis, particularly for accurately isolating tumors and lesions. Effective segmentation improves diagnostic precision and facilitates quantitative analysis, which is vital for medical professionals. However, traditional segmentation methods often struggle with multilevel thresholding due to the associated computational complexity. Therefore, determining the optimal threshold set is an NP-hard problem, highlighting the pressing need for efficient optimization strategies to overcome these challenges. This paper introduces a multi-threshold image segmentation (MTIS) method that integrates a hybrid approach combining Differential Evolution (DE) and the Crayfish Optimization Algorithm (COA), known as HADECO. Utilizing two-dimensional (2D) Kapur’s entropy and a 2D histogram, this method aims to enhance the efficiency and accuracy of subsequent image analysis and diagnosis. HADECO is a hybrid algorithm that combines DE and COA by exchanging information based on predefined rules, leveraging the strengths of both for superior optimization results. It employs Latin Hypercube Sampling (LHS) to generate a high-quality initial population. HADECO introduces an improved DE algorithm (IDE) with adaptive and dynamic adjustments to key DE parameters and new mutation strategies to enhance its search capability. In addition, it incorporates an adaptive COA (ACOA) with dynamic adjustments to the switching probability parameter, effectively balancing exploration and exploitation. To evaluate the effectiveness of HADECO, its performance is initially assessed using CEC’22 benchmark functions. HADECO is evaluated against several contemporary algorithms using the Wilcoxon signed rank test (WSRT) and the Friedman test (FT) to integrate the results. The findings highlight HADECO’s superior optimization abilities, demonstrated by its lowest average Friedman ranking of 1.08. Furthermore, the HADECO-based MTIS method is evaluated using MRI images for knee and CT scans for brain intracranial hemorrhage (ICH). Quantitative results in brain hemorrhage image segmentation show that the proposed method achieves a superior average peak signal-to-noise ratio (PSNR) and feature similarity index (FSIM) of 1.5 and 1.7 at the 6-level threshold. In knee image segmentation, it attains an average PSNR and FSIM of 1.3 and 1.2 at the 5-level threshold, demonstrating the method’s effectiveness in solving image segmentation problems.

Innovative Noise Extraction and Denoising in Low-Dose CT Using a Supervised Deep Learning Framework

Low-dose computed tomography (LDCT) imaging is a critical tool in medical diagnostics due to its reduced radiation exposure. However, this reduction often results in increased noise levels, compromising image quality and diagnostic accuracy. Despite advancements in denoising techniques, a robust method that effectively balances noise reduction and detail preservation remains a significant need. Current denoising algorithms frequently fail to maintain the necessary balance between suppressing noise and preserving crucial diagnostic details. Addressing this gap, our study focuses on developing a deep learning-based denoising algorithm that enhances LDCT image quality without losing essential diagnostic information. Here we present a novel supervised learning-based LDCT denoising algorithm that employs innovative noise extraction and denoising techniques. Our method significantly enhances LDCT image quality by incorporating multiple attention mechanisms within a U-Net-like architecture. Our approach includes a noise extraction network designed to capture diverse noise patterns precisely. This network is integrated into a comprehensive denoising system consisting of a generator network, a discriminator network, and a feature extraction AutoEncoder network. The generator network removes noise and produces high-quality CT images, while the discriminator network differentiates real images from denoised ones, improving the realism of the outputs. The AutoEncoder network ensures the preservation of image details and diagnostic integrity. Our method improves the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) by 7.777 and 0.128 compared to LDCT, by 0.483 and 0.064 compared to residual encoder–decoder convolutional neural network (RED-CNN), by 4.101 and 0.017 compared to Wasserstein generative adversarial network–visual geometry group (WGAN-VGG), and by 3.895 and 0.011 compared to Wasserstein generative adversarial network–autoencoder (WGAN-AE). This demonstrates that our method has a significant advantage in enhancing the signal-to-noise ratio of images. Extensive experiments on multiple standard datasets demonstrate our method’s superior performance in noise suppression and image quality enhancement compared to existing techniques. Our findings significantly impact medical imaging, particularly improving LDCT scan diagnostic accuracy. The enhanced image clarity and detail preservation offered by our method open new avenues for clinical applications and research. This improvement in LDCT image quality promises substantial contributions to clinical diagnostics, disease detection, and treatment planning, ensuring high-quality diagnostic outcomes while minimizing patient radiation exposure.

Retinex theory-based nonlinear luminance enhancement and denoising for low-light endoscopic images

BackgroundThe quality of low-light endoscopic images involves applications in medical disciplines such as physiology and anatomy for the identification and judgement of tissue structures. Due to the use of point light sources and the constraints of narrow physiological structures, medical endoscopic images display uneven brightness, low contrast, and a lack of texture information, presenting diagnostic challenges for physicians.MethodsIn this paper, a nonlinear brightness enhancement and denoising network based on Retinex theory is designed to improve the brightness and details of low-light endoscopic images. The nonlinear luminance enhancement module uses higher-order curvilinear functions to improve overall brightness; the dual-attention denoising module captures detailed features of anatomical structures; and the color loss function mitigates color distortion.ResultsExperimental results on the Endo4IE dataset demonstrate that the proposed method outperforms existing state-of-the-art methods in terms of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS). The PSNR is 27.2202, SSIM is 0.8342, and the LPIPS is 0.1492. It provides a method to enhance image quality in clinical diagnosis and treatment.ConclusionsIt offers an efficient method to enhance images captured by endoscopes and offers valuable insights into intricate human physiological structures, which can effectively assist clinical diagnosis and treatment.

Synthesizing High b-Value Diffusion-Weighted Imaging of Gastric Cancer Using an Improved Vision Transformer CycleGAN.

Diffusion-weighted imaging (DWI), a pivotal component of multiparametric magnetic resonance imaging (mpMRI), plays a pivotal role in the detection, diagnosis, and evaluation of gastric cancer. Despite its potential, DWI is often marred by substantial anatomical distortions and sensitivity artifacts, which can hinder its practical utility. Presently, enhancing DWI's image quality necessitates reliance on cutting-edge hardware and extended scanning durations. The development of a rapid technique that optimally balances shortened acquisition time with improved image quality would have substantial clinical relevance. This study aims to construct and evaluate the unsupervised learning framework called attention dual contrast vision transformer cyclegan (ADCVCGAN) for enhancing image quality and reducing scanning time in gastric DWI. The ADCVCGAN framework, proposed in this study, employs high b-value DWI (b = 1200 s/mm2) as a reference for generating synthetic b-value DWI (s-DWI) from acquired lower b-value DWI (a-DWI, b = 800 s/mm2). Specifically, ADCVCGAN incorporates an attention mechanism CBAM module into the CycleGAN generator to enhance feature extraction from the input a-DWI in both the channel and spatial dimensions. Subsequently, a vision transformer module, based on the U-net framework, is introduced to refine detailed features, aiming to produce s-DWI with image quality comparable to that of b-DWI. Finally, images from the source domain are added as negative samples to the discriminator, encouraging the discriminator to steer the generator towards synthesizing images distant from the source domain in the latent space, with the goal of generating more realistic s-DWI. The image quality of the s-DWI is quantitatively assessed using metrics such as the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), mean squared error (MSE), weighted peak signal-to-noise ratio (WPSNR), and weighted mean squared error (WMSE). Subjective evaluations of different DWI images were conducted using the Wilcoxon signed-rank test. The reproducibility and consistency of b-ADC and s-ADC, calculated from b-DWI and s-DWI, respectively, were assessed using the intraclass correlation coefficient (ICC). A statistical significance level of p < 0.05 was considered. The s-DWI generated by the unsupervised learning framework ADCVCGAN scored significantly higher than a-DWI in quantitative metrics such as PSNR, SSIM, FSIM, MSE, WPSNR, and WMSE, with statistical significance (p < 0.001). This performance is comparable to the optimal level achieved by the latest synthetic algorithms. Subjective scores for lesion visibility, image anatomical details, image distortion, and overall image quality were significantly higher for s-DWI and b-DWI compared to a-DWI (p < 0.001). At the same time, there was no significant difference between the scores of s-DWI and b-DWI (p > 0.05). The consistency of b-ADC and s-ADC readings was comparable among different readers (ICC: b-ADC 0.87-0.90; s-ADC 0.88-0.89, respectively). The repeatability of b-ADC and s-ADC readings by the same reader was also comparable (Reader1 ICC: b-ADC 0.85-0.86, s-ADC 0.85-0.93; Reader2 ICC: b-ADC 0.86-0.87, s-ADC 0.89-0.92, respectively). ADCVCGAN shows excellent promise in generating gastric cancer DWI images. It effectively reduces scanning time, improves image quality, and ensures the authenticity of s-DWI images and their s-ADC values, thus providing a basis for assisting clinical decision making.

Efficient three layer secured adaptive video steganography method using chaotic dynamic systems

In light of the unprecedented growth in internet usage, safeguarding data from unauthorized access has emerged as a paramount concern. Cryptography and steganography stand as pivotal methods for ensuring data security during transmission. This study introduces an innovative adaptive video steganography approach featuring three tiers of security for extracting concealed information, thereby facilitating secure communication. The embedding process operates within the spatial domain of cover video frames, enabling a remarkable hiding ratio of up to 28.125% (equivalent to 2.25 bits per pixel in payload) without compromising the quality of video frames. Users are afforded the flexibility to select between partial or full embedding capacity of CVF through the proposed adaptive control block (ACB). The chaotic key generator (CKG), which combines a logistic map and sine map, is employed to generate highly sensitive initial seeds for permutation order (PO), frame selection (FS), and random position for hiding (RPH), thereby ensuring three levels of security. Prior to transmission, both CVF and hidden data (SD) are encrypted using PO. Encrypted CVFs are then randomly selected using FS for embedding, with RPH employed during the embedding process. Subsequently, for transmitting the stego-video frame, embedded CVFs are decrypted using the same PO. Experimental results demonstrate the efficacy of the proposed approach, achieving an adaptive hiding ratio ranging from 7.1 to 28.125% (equivalent to 0.56 to 2.25 bits per pixel in payload) and maintaining a peak signal-to-noise ratio (PSNR) within the range of 50.25 to 62.05 dB.

Parameter-free super-resolution structured illumination microscopy via a physics-enhanced neural network.

With full-field imaging and high photon efficiency advantages, structured illumination microscopy (SIM) is one of the most potent super-resolution (SR) modalities in bioscience. Regarding SR reconstruction for SIM, spatial domain reconstruction (SDR) has been proven to be faster than traditional frequency domain reconstruction (FDR), facilitating real-time imaging of live cells. Nevertheless, SDR relies on high-precision parameter estimation for reconstruction, which tends to suffer from low signal-to-noise ratio (SNR) conditions and inevitably leads to artifacts that seriously affect the accuracy of SR reconstruction. In this Letter, a physics-enhanced neural network-based parameter-free SDR (PNNP-SDR) is proposed, which can achieve SR reconstruction directly in the spatial domain. As a result, the peak-SNR (PSNR) of PNNP-SDR is improved by about 4 dB compared to the cross-correlation (COR) SR reconstruction; meanwhile, the reconstruction speed of PNNP-SDR is even about five times faster than the fast approach based on principal component analysis (PCA). Given its capability of achieving parameter-free imaging, noise robustness, and high-fidelity and high-speed SR reconstruction over conventional SIM microscope hardware, the proposed PNNP-SDR is expected to be widely adopted in biomedical SR imaging scenarios.

Spatiotemporal correlation enhanced real-time 4D-CBCT imaging using convolutional LSTM networks.

To enhance the accuracy of real-time four-dimensional cone beam CT (4D-CBCT) imaging by incorporating spatiotemporal correlation from the sequential projection image into the single projection-based 4D-CBCT estimation process. We first derived 4D deformation vector fields (DVFs) from patient 4D-CT. Principal component analysis (PCA) was then employed to extract distinctive feature labels for each DVF, focusing on the first three PCA coefficients. To simulate a wide range of respiratory motion, we expanded the motion amplitude and used random sampling to generate approximately 900 sets of PCA labels. These labels were used to produce 900 simulated 4D-DVFs, which in turn deformed the 0% phase 4D-CT to obtain 900 CBCT volumes with continuous motion amplitudes. Following this, the forward projection was performed at one angle to get all of the digital reconstructed radiographs (DRRs). These DRRs and the PCA labels were used as the training data set. To capture the spatiotemporal correlation in the projections, we propose to use the convolutional LSTM (ConvLSTM) network for PCA coefficient estimation. For network testing, when several online CBCT projections (with different motion amplitudes that cover the full respiration range) are acquired and sent into the network, the corresponding 4D-PCA coefficients will be obtained and finally lead to a full online 4D-CBCT prediction. A phantom experiment is first performed with the XCAT phantom; then, a pilot clinical evaluation is further conducted. Results on the XCAT phantom and the patient data show that the proposed approach outperformed other networks in terms of visual inspection and quantitative metrics. For the XCAT phantom experiment, ConvLSTM achieves the highest quantification accuracy with MAPE(Mean Absolute Percentage Error), PSNR (Peak Signal-to-Noise Ratio), and RMSE(Root Mean Squared Error) of 0.0459, 64.6742, and 0.0011, respectively. For the patient pilot clinical experiment, ConvLSTM also achieves the best quantification accuracy with that of 0.0934, 63.7294, and 0.0019, respectively. The quantification evaluation labels that we used are 1) the Mean Absolute Error (MAE), 2) the Normalized Cross Correlation (NCC), 3)the Structural Similarity Index Measurement(SSIM), 4)the Peak Signal-to-Noise Ratio (PSNR), 5)the Root Mean Squared Error(RMSE), and 6) the Absolute Percentage Error (MAPE). The spatiotemporal correlation-based respiration motion modeling supplied a potential solution for accurate real-time 4D-CBCT reconstruction.