Facial video-based remote physiological measurement (rPPG) can non-invasively estimate vital signs, such as heart rate (HR), which often faces challenges under varying lighting conditions. We propose the PhysLight framework to enhance the accuracy of rPPG heart rate measurement through adaptive video relighting. Our approach subtly modifies illumination in video frames to improve detection accuracy while maintaining visual quality. The framework includes a GenLightNet to extract ideal lighting priors and a WipeLightNet module to refine poorly lit videos. Extensive evaluations on benchmark datasets show that our method significantly improves HR estimation reliability, outperforming existing baselines and enhancing non-contact physiological monitoring in diverse environments.

ABSTRACT To solve efficient image segmentation in practical medical applications in resource‐constrained point‐of‐care environments, the lightweight medical image segmentation network is proposed based on grouped feature fusion and large kernel convolution, which introduces a U‐shaped, convolution‐based architecture that significantly reduces parameters and computational cost. The proposed model combines shifted large kernel convolution with grouped feature fusion technique in a lightweight and attention‐free way, which is specifically designed to fuse features to capture global context. Meanwhile, the grouped multi‐scale feature fusion module is proposed to achieve effective cross‐layer connectivity and efficient fusion of multi‐scale features by grouping deep and shallow features and subsequently applying a lightweight grouped large kernel convolution. The extensive experiments on multiple datasets verify that our model outperforms current popular models in image segmentation with lower parameter quantity and computational cost, and achieves industry‐leading performance with low resource consumption.

ABSTRACT Fundus photography provides imaging techniques for the diagnosis of retinal diseases. The diagnostic accuracy, however, heavily relies on the clarity of subtle lesions, which can be significantly affected by image resolution. Achieving a balance between reconstruction quality, model complexity, and training efficiency remains a key challenge, particularly under limited data conditions. To address these issues, a lightweight end‐to‐end model LiteZSSR is proposed for super‐resolution reconstruction of fundus images, incorporating a residual information distillation module to extract multi‐scale features within a shallow network architecture, effectively retaining both local and global contextual information. In addition, a multi‐feature fusion group composed of multiple large kernel attention blocks is designed to strengthen feature representation while minimizing redundancy and computational overhead. Unsupervised training based on internal image learning is adopted to eliminate dependence on large‐scale datasets and to suppress artifacts commonly produced by CNN‐based SRR methods. Extensive experiments on publicly available fundus image datasets, including DRIVE, STARE, and CHASEDB1, demonstrate that LiteZSSR outperforms existing state‐of‐the‐art methods in terms of PSNR and SSIM, while significantly reducing model parameters. These results highlight its potential for practical deployment in clinical fundus image enhancement tasks.

Recently, ShiftViT and its variants have attracted much attention for their simple and efficient shift operation, showing excellent efficacy in several tasks on natural images, surpassing Swin Transformer. However, considering the complexity of 3D multimodal images, which have higher dimensions than natural images, and the relative stability of the human tissue structure in medical images, the applicability of shift operation on 3D multimodal medical data has yet to be determined. This paper demonstrates that ShiftViT has enormous potential in 3D multimodal medical image analysis. Using 3D medical image segmentation as a representative downstream task, we investigate how shift operation can improve model performance. First, applying ShiftViT to 3D multimodal medical images not only effectively extracts global information but also significantly enhances the model's performance. Second, as a plug-and-play strategy, the shift operation can be integrated with other modules without adding additional computational burden, proving its flexibility in the overall system. Finally, we further investigate the generalizability of the shift operation by introducing a cascaded attention module, which provides useful insights to improve the generalizability of 3D medical image segmentation models. Through this study, we extend the application scope of ShiftViT and bring new exploration directions to the field of 3D multimodal medical image analysis. Our research results prove the feasibility of applying ShiftViT in 3D multimodal medical images and provide an effective and scalable model, which is expected to further promote the development of medical image processing technology.

Abstract This paper proposes a super‐resolution reconstruction model, SRPGANto improve the visual quality of images based on generative adversarial networks (GANs) by improving the network structures of the generator and the discriminator. In the generator, a dual branch residual block is designed instead of the residual block, including a branch with an attention mechanism and a branch without an attention mechanism, to extract more differentiated features. Normalization methods are explored to avoid unstable training and bath normalization artifacts and use a half instance normalization layer that is more suitable for underlying visual problems compared with traditional batch normalization. In the discriminator, PatchGAN is applied instead of typical GAN to improve the generation of local texture by discriminating each patch rather than the global image. The experimental results on the public datasets demonstrate that the proposed SRPGAN can achieve excellent quantitative evaluation while improving the visual quality of reconstructed images.

Localization is vital and fundamental for underwater acoustic sensor networks (UASNs), as it provides location information for UASNs to achieve various practical underwater tasks. Most existing localization methods assume small-scale scenarios without battery energy constraints, making it inapplicable to large-scale UASNs. In large-scale UASNs, localization suffers from the challenges of excessive energy consumption and large localization error because of harsh underwater conditions like node mobility and huge ranging errors. To this end, we propose an efficient localization scheme with velocity prediction (LSVP) to solve the above challenges for large-scale UASNs. LSVP considers node mobility, ranging errors, and energy balance in a unified framework, which is applicable to realistic and scalable UASNs. Specifically, we first design a Doppler-assisted velocity prediction (DVP) algorithm to decrease energy consumption, which can solve the excessive communications caused by node mobility under ocean currents. Then, a acrlong CIL algorithm is proposed to decrease the localization error, which can reduce location uncertainty and error propagation caused by ranging errors. Extensive simulation results indicate that LSVP can achieve accurate velocity prediction and high precision localization for large-scale UASNs.

Localization is a vital service in underwater acoustic sensor networks (UASNs). Autonomous underwater vehicles (AUVs), with their mobility and collaborations can provide accurate, extensive, and efficient localization service for large-scale UASNs. During localization, AUVs travel along the predefined paths and broadcast reference messages to aid sensor nodes in estimating locations. However, AUVs-aided localization faces the following two challenges: 1) complex localization path planning for multiple AUVs, which requires consideration of localization accuracy and optimization of travel path simultaneously and 2) harsh underwater localization conditions, such as unsynchronized clocks and stratification effects seriously affect the localization accuracy. To this end, an efficient AUVs-aided localization scheme (EAL) is proposed for large-scale UASNs, which jointly addresses the path planning and localization in an unified framework. Specifically, we propose a graph-based localization path planning mechanism, which considers the impact of path on localization and determines effective travel paths for AUVs. Furthermore, we design an iteration-based asynchronous localization mechanism, which could compensate the stratification effect and achieve accurate localization for the sensor nodes. Extensive simulation results show that the EAL can achieve efficient and high accuracy localization for the sensor nodes with the aid of multiple AUVs.

Underwater wireless sensor networks (UWSNs) can provide services to the ocean. The deployment is one of the key problems in UWSNs. Optimizing networks power consumption and coverage has always been a huge challenge in deployment. Existing deployment models do not consider the asymmetric-link phenomenon and power control. In this paper, a new multi-objective optimization deployment model with power control in UWSNs is proposed to obtain a deployment scheme, which reduces the power consumption, and the asymmetric-link phenomenon is considered. At the same time, due to the unsatisfactory optimization performance and stability of some current algorithms, this paper proposes an algorithm named Crow-Colony Search Optimization Algorithm (C-CSOA) to optimize the deployment scheme. In this algorithm, we adopt the framework of Colony Search Optimization Algorithm (CSOA). In addition, we combine the advantages of Crow Search Algorithm (CSA) and CSOA to improve the optimization performance of the algorithm. We conducted simulation experiments, and the results indicate that: First, the model we proposed is feasible. Second, an efficient deployment scheme can be obtained by C-CSOA. Third, when reaching the predetermined network coverage, the total power consumption of UWSNs is significantly reduced (nearly 23.93% in average). By comparing with various advanced optimization algorithms, it is showed that C-CSOA has advantages of the good optimization performance and the small standard deviation.

Automatic fovea localization is a challenging issue. In this article, we focus on the study of fovea localization and propose a robust fovea localization method. We propose concentric circular sectional symmetry measure (CCSSM) for symmetry axis detection, and region of interest (ROI) determination, which is a global feature descriptor robust against local feature changes, to solve the lesion interference issue, i.e., fovea visibility interference from lesions, using both structure features and morphological features. We propose the index of convexity and concavity (ICC) as the convexity-concavity measure of the surface and provide a quantitative evaluation tool for ophthalmologists to learn whether the occurrence of lesion within the ROI. We propose the weighted gradient accumulation map, which is insensitive to local intensity changes and can overcome the influence of noise and contamination, to perform refined localization. The advantages of the proposed method lies in two aspects. First, the accuracy and robustness can be achieved without typical sophisticated manner, i.e., blood vessel segmentation and parabola fitting. Second, the lesion interference is considered in our plan of fovea localization. Our proposed symmetry-based method is innovative in the solution of fovea detection, and it is simple, practical, and controllable. Experiment results show that the proposed method can resist the interference of unbalanced illumination and lesions, and achieve high accuracy rate in five datasets. Compared to the state-of-the-art methods, high robustness and accuracy of the proposed method guarantees its reliability.

Intensity degradations are a familiar problem for fluorescein angiogram sequences. In this paper, we attempt to restore a noisy fluorescein angiogram, and to keep the high intensity pixels from degrading. To this end, we incorporate a new constraint, called intensity constraint, to Miller's regularization formulation with a smoothness constraint. Considering the specified requirement for fluorescein angiograms, we also modify the Q-th order converging algorithm for implementation purpose. In our scheme, including its formulation and implementation, image restoration can not only handle the traditional problems, such as blur and noise, but also achieve an important feature, intensity preservation. The experiments show that our approach has satisfactory results in the two aspects.

In this paper, a novel adaptive soft morphological gradient (ASMG) filter is proposed, based on a combination of the idea of the soft morphological filtering and the adaptive technique. ASMG filtering is an efficient nonlinear sharpening method, which can be applied for edge detection. By employing four directional structuring elements, ASMG filtering has the capability of selecting the directional structuring element with the maximum response, whose direction varies depending on the change of directional edges. In addition, by comparing the variance of the moving structuring window with the base variance, the ASMG filter provides an adaptive algorithm for morphological operations. The experimental results show that the ASMG filter is better than the traditional operators in edge detection and noise suppression.