Average Structural Similarity Research Articles

Simulation study on magneto-acoustic concentration tomography of magnetic nanoparticles based on vectorial acoustic source and BICGSTAB method

Abstract To overcome the vulnerability to noise of the reconstructed image and simplify the cumbersome iteration process of algorithm in the magneto-acoustic concentration tomography with magnetic induction (MACT-MI) for magnetic nanoparticles (MNPs), we established the matrix relationship between the concentration of MNPs and the first-order derivative of sound pressure based on the reconstruction method of vectorial acoustic source, and proposed the application of BICGSTAB method in solving the concentration distribution. Firstly, a simulation model was established in COMSOL Multiphysics. Secondly, the obtained data were substituted into the derived formula for imaging reconstruction. Finally, the quality of the reconstructed image was analyzed. The effects of MNP radius, shape, asymptotic concentration, and SNR on the reconstruction results were studied. Simulation results show that under the same noise condition, compared with the reconstruction method based on the LSQR-trapezoidal method, the average correlation coefficient increased by 32.9%, the average relative error decreased by 48.5%, the average structural similarity increased by 48.2%, and the average iterations decreased by 58.5%. The proposed method shows superior imaging quality and noise immunity. The research provides a theoretical basis for subsequent experimental research.

Computational staining of CD3/CD20 positive lymphocytes in human tissues with experimental confirmation in a genetically engineered mouse model

IntroductionImmune dysregulation plays a major role in cancer progression. The quantification of lymphocytic spatial inflammation may enable spatial system biology, improve understanding of therapeutic resistance, and contribute to prognostic imaging biomarkers.MethodsIn this paper, we propose a knowledge-guided deep learning framework to measure the lymphocytic spatial architecture on human H&E tissue, where the fidelity of training labels is maximized through single-cell resolution image registration of H&E to IHC. We demonstrate that such an approach enables pixel-perfect ground-truth labeling of lymphocytes on H&E as measured by IHC. We then experimentally validate our technique in a genetically engineered, immune-compromised Rag2 mouse model, where Rag2 knockout mice lacking mature lymphocytes are used as a negative experimental control. Such experimental validation moves beyond the classical statistical testing of deep learning models and demonstrates feasibility of more rigorous validation strategies that integrate computational science and basic science.ResultsUsing our developed approach, we automatically annotated more than 111,000 human nuclei (45,611 CD3/CD20 positive lymphocytes) on H&E images to develop our model, which achieved an AUC of 0.78 and 0.71 on internal hold-out testing data and external testing on an independent dataset, respectively. As a measure of the global spatial architecture of the lymphocytic microenvironment, the average structural similarity between predicted lymphocytic density maps and ground truth lymphocytic density maps was 0.86 ± 0.06 on testing data. On experimental mouse model validation, we measured a lymphocytic density of 96.5 ± %1% in a Rag2+/- control mouse, compared to an average of 16.2 ± %5% in Rag2-/- immune knockout mice (p<0.0001, ANOVA-test).DiscussionThese results demonstrate that CD3/CD20 positive lymphocytes can be accurately detected and characterized on H&E by deep learning and generalized across species. Collectively, these data suggest that our understanding of complex biological systems may benefit from computationally-derived spatial analysis, as well as integration of computational science and basic science.

Holo-U2Net for High-Fidelity 3D Hologram Generation.

Traditional methods of hologram generation, such as point-, polygon-, and layer-based physical simulation approaches, suffer from substantial computational overhead and generate low-fidelity holograms. Deep learning-based computer-generated holography demonstrates effective performance in terms of speed and hologram fidelity. There is potential to enhance the network's capacity for fitting and modeling in the context of computer-generated holography utilizing deep learning methods. Specifically, the ability of the proposed network to simulate Fresnel diffraction based on the provided hologram dataset requires further improvement to meet expectations for high-fidelity holograms. We propose a neural architecture called Holo-U2Net to address the challenge of generating a high-fidelity hologram within an acceptable time frame. Holo-U2Net shows notable performance in hologram evaluation metrics, including an average structural similarity of 0.9988, an average peak signal-to-noise ratio of 46.75 dB, an enhanced correlation coefficient of 0.9996, and a learned perceptual image patch similarity of 0.0008 on the MIT-CGH-4K large-scale hologram dataset.

A pyramid-style neural network model with alterable input for reconstruction of physics field on turbine blade surface from various sparse measurements

Data from turbine cascade experiments typically exhibits low spatial–temporal resolution, along with inevitable noise and local data missing. This paper aims to establish a super-resolution model to reconstruct the complete pressure and temperature fields on the blade surface from limited observations, using the deep learning method. Depending on the three-dimensional geometric complexity of the blade, the required cross-sectional data varies, resulting in input data of different sizes. Conventional surrogate models typically confine themselves to a single input format, leading to limited compatibility with diverse inputs. A pyramid-style model with four optional input ports is proposed, designed to accommodate data with different numbers of span-wise sections. Three training strategies have been evaluated, where distributed training has been proven to be more time-effective and flexible while maintaining high prediction precision, compared to holistic and conventional training. Generally, the average relative error over the whole dataset falls below 0.18%, the average peak signal-to-noise ratio exceeds 52 dB, and the average structural similarity index measurement surpasses 0.999. As the number of span-wise sections and the amount of information in the input increase, the overall performance shows a consistent upward trend. Robustness analyses are conducted by applying artificial noises of varying intensities. The results indicate that the model can tolerate noise intensities of up to 5% with a satisfactory reconstruction accuracy. In addition, the model is quite sensitive to the measurement data noises in complex flow regions such as expansion waves, suggesting that more intensive measurements should be targeted to those regions when conducting cascade experiments. The proposed method satisfies the intended objectives and provides an idea for future applications in digital twin platforms.

Reconstructing images of objects: method for reconstructing images from digital off-axis holograms based on a generative adversarial neural network

The reconstruction of object images that are located in 3D scene cross-sections using digital holography is described. The potential of generative adversarial networks for reconstructing cross-sections of 3D scenes composed of multiple layers of off-axis objects from holograms is investigated. Such scenes consist of a series of sections with objects that are not aligned with the camera’s axis. Digital holograms were used to reconstruct images of cross-sectional views of 3D scenes. It has been shown that the use of neural networks increases the speed and reconstruction quality, and reduces the image noise. A method for reconstructing images of objects using digital off-axis holograms and a generative adversarial neural network is proposed. The proposed method was tested on both numerically simulated and experimentally captured digital holograms. It was able to successfully reconstruct up to 8 cross-sections of a 3D scene from a single hologram. It was obtained that an average structural similarity index measure was equal to at least 0.73. Based on optically registered holograms, the method allowed us to reconstruct object image cross-sections of a 3D scene with a structural similarity index measure over cross-sections of a 3D scene of equal to 0.83. Therefore, the proposed technique provides the possibility for high-quality object image reconstruction and could be utilized in the analysis of micro- and macroobjects, including medical and biological applications, metrology, characterization of materials, surfaces, and volume media.

Deep learning decryption approach for asymmetric computer-generated holography (CGH) cryptosystem

Deep-learning-based optical image decryption has attracted attention due to its remarkable advantages of keyless managements. Here, a high-fidelity deep learning (DL) decryption strategy is proposed, aiming for the asymmetric DRPE-based CGH cryptosystem, which is combined with phase truncation technique and chaotic iris phase masks. First, a mass of ciphertext and plaintext image pairs are generated to create a dataset. Then, a deep neural network, namely ACGHC-Net (network for the asymmetric DRPE-based CGH cryptosystem), is designed and trained in a supervised learning manner. After the model training and tuning, the ACGHC-Net can quickly and accurately decrypt the ciphertext images. The average cross-correlation coefficient (CC) of the decrypted images achieves 0.998, the average structural similarity (SSIM) 0.895, and the average peak signal-to-noise ratio (PSNR) 31.090 dB. Furthermore, we conducted anti-noise and anti-clipping analysis on the ACGHC-Net. The results prove that the proposed ACGHC-Net can successfully decrypt the encrypted complex grayscale images, and has good anti-noise and anti-cropping robustness for the asymmetric DRPE-based CGH cryptosystem. The proposed method will be expected to further boost keyless decryption in image encryption systems.

Deep Learning Based Cystoscopy Image Enhancement.

Background: Endoscopy image enhancement technology provides doctors with clearer and more detailed images for observation and diagnosis, allowing doctors to assess lesions more accurately. Unlike most other endoscopy images, cystoscopy images face more complex and diverse image degradation because of their underwater imaging characteristics. Among the various causes of image degradation, the blood haze resulting from bladder mucosal bleeding make the background blurry and unclear, severely affecting diagnostic efficiency, even leading to misjudgment. Materials and Methods: We propose a deep learning-based approach to mitigate the impact of blood haze on cystoscopy images. The approach consists of two parts as follows: a blood haze removal network and a contrast enhancement algorithm. First, we adopt Feature Fusion Attention Network (FFA-Net) and transfer learning in the field of deep learning to remove blood haze from cystoscopy images and introduce perceptual loss to constrain the network for better visual results. Second, we enhance the image contrast by remapping the gray scale of the blood haze-free image and performing weighted fusion of the processed image and the original image. Results: In the blood haze removal stage, the algorithm proposed in this article achieves an average peak signal-to-noise ratio of 29.44 decibels, which is 15% higher than state-of-the-art traditional methods. The average structural similarity and perceptual image patch similarity reach 0.9269 and 0.1146, respectively, both superior to state-of-the-art traditional methods. Besides, our method is the best in keeping color balance after removing the blood haze. In the image enhancement stage, our algorithm enhances the contrast of vessels and tissues while preserving the original colors, expanding the dynamic range of the image. Conclusion: The deep learning-based cystoscopy image enhancement method is significantly better than other traditional methods in both qualitative and quantitative evaluation. The application of artificial intelligence will provide clearer, higher contrast cystoscopy images for medical diagnosis.

Design of floating point multiplier using approximate hybrid Radix-4/ Radix-8 booth encoder for image analysis

This paper presents a novel power efficient Hybrid floating point multiplier (HFPM) with approximate hybrid radix-4/radix-8 booth encoder. This approach efficiently increases the speed of operation and optimises hardware. Compared to existing HFPMs, the proposed HFPM utilises a novel hybrid radix-4/radix-8 booth encoder. The hybrid architecture reduces the computations by an average of k/3 for a k-bit 2’s complement representation by reducing the number of partial products. In addition, to achieve an area-efficient and power-efficient design, approximate computational units are added to the radix-8 booth encoder in the hybrid architecture with a slight reduction in accuracy. The efficiency of the approximate architecture is measured in scales of Error Rate (ER) and Normalized Error Distance (NED). The proposed design is implemented on Kintex 7 FPGA and realized on 45nm ASIC platforms. The experimental study shows that the proposed multiplier has utilised hardware (831 slice LUT, 828 LUT as logic) and consumed 6110 μm2 of area and 83 μW of power which is less as compared to existing floating point multipliers. The experimental results in terms of average Structural Similarity Index Measure (SSIM) is 0.98 which is better than the existing floating point multipliers are compared.

Metal Artifact Correction in Industrial CT Images Based on a Dual-Domain Joint Deep Learning Framework

Industrial computed tomography (CT) images reconstructed directly from projection data using the filtered back projection (FBP) method exhibit strong metal artifacts due to factors such as beam hardening, scatter, statistical noise, and deficiencies in the reconstruction algorithms. Traditional correction approaches, confined to either the projection domain or the image domain, fail to fully utilize the rich information embedded in the data. To leverage information from both domains, we propose a joint deep learning framework that integrates UNet and ResNet architectures for the correction of metal artifacts in CT images. Initially, the UNet network is employed to correct the imperfect projection data (sinograms), the output of which serves as the input for the CT image reconstruction unit. Subsequently, the reconstructed CT images are fed into the ResNet, with both networks undergoing a joint training process to optimize image quality. We take the projection data obtained by analytical simulation as the data set. The resulting optimized industrial CT images show a significant reduction in metal artifacts, with the average Peak Signal-to-Noise Ratio (PSNR) reaching 36.13 and the average Structural Similarity Index (SSIM) achieving 0.953. By conducting simultaneous correction in both the projection and image domains, our method effectively harnesses the complementary information from both, exhibiting a marked improvement in correction results over the deep learning-based single-domain corrections. The generalization capability of our proposed method is further verified in ablation experiments and multi-material phantom CT artifact correction.

Hybrid-supervised deep learning for domain transfer 3D protoacoustic image reconstruction

Objective. Protoacoustic imaging showed great promise in providing real-time 3D dose verification of proton therapy. However, the limited acquisition angle in protoacoustic imaging induces severe artifacts, which impairs its accuracy for dose verification. In this study, we developed a hybrid-supervised deep learning method for protoacoustic imaging to address the limited view issue. Approach. We proposed a Recon-Enhance two-stage deep learning method. In the Recon-stage, a transformer-based network was developed to reconstruct initial pressure maps from raw acoustic signals. The network is trained in a hybrid-supervised approach, where it is first trained using supervision by the iteratively reconstructed pressure map and then fine-tuned using transfer learning and self-supervision based on the data fidelity constraint. In the enhance-stage, a 3D U-net is applied to further enhance the image quality with supervision from the ground truth pressure map. The final protoacoustic images are then converted to dose for proton verification. Main results. The results evaluated on a dataset of 126 prostate cancer patients achieved an average root mean squared errors (RMSE) of 0.0292, and an average structural similarity index measure (SSIM) of 0.9618, out-performing related start-of-the-art methods. Qualitative results also demonstrated that our approach addressed the limit-view issue with more details reconstructed. Dose verification achieved an average RMSE of 0.018, and an average SSIM of 0.9891. Gamma index evaluation demonstrated a high agreement (94.7% and 95.7% for 1%/3 mm and 1%/5 mm) between the predicted and the ground truth dose maps. Notably, the processing time was reduced to 6 s, demonstrating its feasibility for online 3D dose verification for prostate proton therapy. Significance. Our study achieved start-of-the-art performance in the challenging task of direct reconstruction from radiofrequency signals, demonstrating the great promise of PA imaging as a highly efficient and accurate tool for in vivo 3D proton dose verification to minimize the range uncertainties of proton therapy to improve its precision and outcomes.

Optimizing anti-perturbation capability in single-shot wide-field multimode fiber imaging systems

In recent years, multimode fiber (MMF) has emerged as a focal point in ultrathin endoscopy owing to its high-capacity information transmission. Nevertheless, the technology's susceptibility to external perturbances limits its practical applications. In this study, we employ a single MMF as both the illumination unit and imaging probe and utilize this single-shot wide-field MMF imaging system to investigate the impact of LED and laser sources on anti-perturbation capabilities. Experimental results demonstrate that, in the absence of deformations in the MMF, both LED and laser-based systems achieve an average structural similarity (SSIM) index of around 0.8 for the reconstructed image, utilizing advanced deep learning techniques, with the laser-based system performing slightly better. However, under unknown MMF configurations post-deformation, the SSIM remains robust at 0.67 for the LED-based system, while the laser-based system drops the average SSIM to 0.45. The results reveal that LED has anti-perturbation capability in single-shot wide-field MMF imaging systems. These findings indicate significant potential for future anti-perturbation studies in endoscopy employing MMF imaging.

Improving photoacoustic imaging through the skull using deep learning: a numerical study

Photoacoustic computed tomography (PACT) has recently emerged as an attractive imaging modality for functional brain imaging due to its rich optical absorption contrast, high spatial and temporal resolutions, and relatively deep penetration. However, a major hurdle in using PACT for the human brain is distortion of the signal due to the skull, which negatively affects the quality of the images. In this project, we aimed to improve transcranial PACT using a U-Net architecture that can minimize distortion from the skull. This numerical study utilized a large collection of blood vessel images obtained from an online database and a computed tomography (CT) scan of an ex vivo human skull. The synthetic photoacoustic radiofrequency data were generated using the open-source wave solver k-Wave. Comparing the images generated by deep learning with the ground truth images, we achieved an average structural similarity index of 0.874 and an average peak signal-to-noise ratio of 17.92 dB.

Video stabilization based on low‐rank constraint and trajectory optimization

AbstractVideo stabilization plays a pivotal role in enhancing video quality by eliminating unwanted jitter in shaky videos. This paper introduces a novel video stabilization algorithm that leverages low‐rank constraint and trajectory optimization to effectively eliminate undesirable motion and generate stabilized videos. In the proposed algorithm, a low‐rank constraint regularization term is incorporated to enhance the smoothness of motion trajectories. Additionally, a predictive path smoothness term is integrated to ensure the consistency of motion across neighbouring frames. To address the problem of excessive cropping resulting from aggressive smoothing, a flexible local window strategy that emphasizes local motion relationships within the trajectories is introduced. The experimental results show that, compared to other excellent video stabilization algorithms, the proposed algorithm improves the stability metric by approximately 2.3%. Furthermore, in the stabilized videos generated by the algorithm, an approximate improvement of 2.18 dB in average image temporal fidelity and a 5.7% increase in average structural similarity between adjacent frames are achieved. The code that implements the proposed method is publicly accessible at https://github.com/CZS0319/VS_Low‐Rank_Constraint_Trajectory_Optimization.

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

XIOSIS: An X-Ray-Based Intra-Operative Image-Guided Platform for Oncology Smart Material Delivery.

Image-guided interventional oncology procedures can greatly enhance the outcome of cancer treatment. As an enhancing procedure, oncology smart material delivery can increase cancer therapy's quality, effectiveness, and safety. However, the effectiveness of enhancing procedures highly depends on the accuracy of smart material placement procedures. Inaccurate placement of smart materials can lead to adverse side effects and health hazards. Image guidance can considerably improve the safety and robustness of smart material delivery. In this study, we developed a novel generative deep-learning platform that highly prioritizes clinical practicality and provides the most informative intra-operative feedback for image-guided smart material delivery. XIOSIS generates a patient-specific 3D volumetric computed tomography (CT) from three intraoperative radiographs (X-ray images) acquired by a mobile C-arm during the operation. As the first of its kind, XIOSIS (i) synthesizes the CT from small field-of-view radiographs;(ii) reconstructs the intra-operative spacer distribution; (iii) is robust; and (iv) is equipped with a novel soft-contrast cost function. To demonstrate the effectiveness of XIOSIS in providing intra-operative image guidance, we applied XIOSIS to the duodenal hydrogel spacer placement procedure. We evaluated XIOSIS performance in an image-guided virtual spacer placement and actual spacer placement in two cadaver specimens. XIOSIS showed a clinically acceptable performance, reconstructed the 3D intra-operative hydrogel spacer distribution with an average structural similarity of 0.88 and Dice coefficient of 0.63 and with less than 1 cm difference in spacer location relative to the spinal cord.

Rapid scanning method for SICM based on autoencoder network

Scanning Ion Conductance Microscopy (SICM) enables non-destructive imaging of living cells, which makes it highly valuable in life sciences, medicine, pharmacology, and many other fields. However, because of the uncertainty retrace height of SICM hopping mode, the time resolution of SICM is relatively low, which makes the device fail to meet the demands of dynamic scanning. To address above issues, we propose a fast-scanning method for SICM based on an autoencoder network. Firstly, we cut under-sampled images into small image lists. Secondly, we feed them into a self-constructed primitive-autoencoder super-resolution network to compute high-resolution images. Finally, the inferred scanning path is determined using the computed images to reconstruct the real high-resolution scanning path. The results demonstrate that the proposed network can reconstruct higher-resolution images in various super-resolution tasks of low-resolution scanned images. Compared to existing traditional interpolation methods, the average peak signal-to-noise ratio improvement is greater than 7.5823 dB, and the average structural similarity index improvement is greater than 0.2372. At the same time, using the proposed method for high-resolution image scanning leads to a 156.25% speed improvement compared to traditional methods. It opens up possibilities for achieving high-time resolution imaging of dynamic samples in SICM and further promotes the widespread application of SICM in the future.

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.

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.

Face Image Segmentation Using Boosted Grey Wolf Optimizer.

Image segmentation methods have received widespread attention in face image recognition, which can divide each pixel in the image into different regions and effectively distinguish the face region from the background for further recognition. Threshold segmentation, a common image segmentation method, suffers from the problem that the computational complexity shows exponential growth with the increase in the segmentation threshold level. Therefore, in order to improve the segmentation quality and obtain the segmentation thresholds more efficiently, a multi-threshold image segmentation framework based on a meta-heuristic optimization technique combined with Kapur's entropy is proposed in this study. A meta-heuristic optimization method based on an improved grey wolf optimizer variant is proposed to optimize the 2D Kapur's entropy of the greyscale and nonlocal mean 2D histograms generated by image computation. In order to verify the advancement of the method, experiments compared with the state-of-the-art method on IEEE CEC2020 and face image segmentation public dataset were conducted in this paper. The proposed method has achieved better results than other methods in various tests at 18 thresholds with an average feature similarity of 0.8792, an average structural similarity of 0.8532, and an average peak signal-to-noise ratio of 24.9 dB. It can be used as an effective tool for face segmentation.