المؤلفون: Xiong, Jiamei, Yan, Xuefeng, Wang, Yongzhen, Zhao, Wei, Zhang, Xiao-Ping, Wei, Mingqiang

مصطلحات موضوعية: Computer Science - Computer Vision and Pattern Recognition

الوصف: Haze severely degrades the visual quality of remote sensing images and hampers the performance of automotive navigation, intelligent monitoring, and urban management. The emerging denoising diffusion probabilistic model (DDPM) exhibits the significant potential for dense haze removal with its strong generation ability. Since remote sensing images contain extensive small-scale texture structures, it is important to effectively restore image details from hazy images. However, current wisdom of DDPM fails to preserve image details and color fidelity well, limiting its dehazing capacity for remote sensing images. In this paper, we propose a novel unified Fourier-aware diffusion model for remote sensing image dehazing, termed RSHazeDiff. From a new perspective, RSHazeDiff explores the conditional DDPM to improve image quality in dense hazy scenarios, and it makes three key contributions. First, RSHazeDiff refines the training phase of diffusion process by performing noise estimation and reconstruction constraints in a coarse-to-fine fashion. Thus, it remedies the unpleasing results caused by the simple noise estimation constraint in DDPM. Second, by taking the frequency information as important prior knowledge during iterative sampling steps, RSHazeDiff can preserve more texture details and color fidelity in dehazed images. Third, we design a global compensated learning module to utilize the Fourier transform to capture the global dependency features of input images, which can effectively mitigate the effects of boundary artifacts when processing fixed-size patches. Experiments on both synthetic and real-world benchmarks validate the favorable performance of RSHazeDiff over multiple state-of-the-art methods. Source code will be released at https://github.com/jm-xiong/RSHazeDiffTest.

الوصول الحر: http://arxiv.org/abs/2405.09083Test

المؤلفون: Fan, Yihua, Wang, Yongzhen, Wei, Mingqiang, Wang, Fu Lee, Xie, Haoran

مصطلحات موضوعية: Computer Science - Computer Vision and Pattern Recognition

الوصف: Adverse weather conditions often impair the quality of captured images, inevitably inducing cutting-edge object detection models for advanced driver assistance systems (ADAS) and autonomous driving. In this paper, we raise an intriguing question: can the combination of image restoration and object detection enhance detection performance in adverse weather conditions? To answer it, we propose an effective architecture that bridges image dehazing and object detection together via guidance information and task-driven learning to achieve detection-friendly dehazing, termed FriendNet. FriendNet aims to deliver both high-quality perception and high detection capacity. Different from existing efforts that intuitively treat image dehazing as pre-processing, FriendNet establishes a positive correlation between these two tasks. Clean features generated by the dehazing network potentially contribute to improvements in object detection performance. Conversely, object detection crucially guides the learning process of the image dehazing network under the task-driven learning scheme. We shed light on how downstream tasks can guide upstream dehazing processes, considering both network architecture and learning objectives. We design Guidance Fusion Block (GFB) and Guidance Attention Block (GAB) to facilitate the integration of detection information into the network. Furthermore, the incorporation of the detection task loss aids in refining the optimization process. Additionally, we introduce a new Physics-aware Feature Enhancement Block (PFEB), which integrates physics-based priors to enhance the feature extraction and representation capabilities. Extensive experiments on synthetic and real-world datasets demonstrate the superiority of our method over state-of-the-art methods on both image quality and detection precision. Our source code is available at https://github.com/fanyihua0309/FriendNetTest.
Comment: 13 pages, 8 figures, 6 tables

الوصول الحر: http://arxiv.org/abs/2403.04443Test

المؤلفون: Tong, Ming, Yan, Xuefeng, Wang, Yongzhen

مصطلحات موضوعية: Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing

الوصف: Visual-based measurement systems are frequently affected by rainy weather due to the degradation caused by rain streaks in captured images, and existing imaging devices struggle to address this issue in real-time. While most efforts leverage deep networks for image deraining and have made progress, their large parameter sizes hinder deployment on resource-constrained devices. Additionally, these data-driven models often produce deterministic results, without considering their inherent epistemic uncertainty, which can lead to undesired reconstruction errors. Well-calibrated uncertainty can help alleviate prediction errors and assist measurement devices in mitigating risks and improving usability. Therefore, we propose an Uncertainty-Driven Multi-Scale Feature Fusion Network (UMFFNet) that learns the probability mapping distribution between paired images to estimate uncertainty. Specifically, we introduce an uncertainty feature fusion block (UFFB) that utilizes uncertainty information to dynamically enhance acquired features and focus on blurry regions obscured by rain streaks, reducing prediction errors. In addition, to further boost the performance of UMFFNet, we fused feature information from multiple scales to guide the network for efficient collaborative rain removal. Extensive experiments demonstrate that UMFFNet achieves significant performance improvements with few parameters, surpassing state-of-the-art image deraining methods.

الوصول الحر: http://arxiv.org/abs/2307.09728Test

المؤلفون: Wang, Yongzhen, Yan, Xuefeng, Niu, Yanbiao, Gong, Lina, Guo, Yanwen, Wei, Mingqiang

مصطلحات موضوعية: Computer Science - Computer Vision and Pattern Recognition

الوصف: Rainy weather significantly deteriorates the visibility of scene objects, particularly when images are captured through outdoor camera lenses or windshields. Through careful observation of numerous rainy photos, we have found that the images are generally affected by various rainwater artifacts such as raindrops, rain streaks, and rainy haze, which impact the image quality from both near and far distances, resulting in a complex and intertwined process of image degradation. However, current deraining techniques are limited in their ability to address only one or two types of rainwater, which poses a challenge in removing the mixture of rain (MOR). In this study, we propose an effective image deraining paradigm for Mixture of rain REmoval, called DEMore-Net, which takes full account of the MOR effect. Going beyond the existing deraining wisdom, DEMore-Net is a joint learning paradigm that integrates depth estimation and MOR removal tasks to achieve superior rain removal. The depth information can offer additional meaningful guidance information based on distance, thus better helping DEMore-Net remove different types of rainwater. Moreover, this study explores normalization approaches in image deraining tasks and introduces a new Hybrid Normalization Block (HNB) to enhance the deraining performance of DEMore-Net. Extensive experiments conducted on synthetic datasets and real-world MOR photos fully validate the superiority of the proposed DEMore-Net. Code is available at https://github.com/yzTest-wang/DEMore-Net.
Comment: 11 pages, 7 figures, 5 tables

الوصول الحر: http://arxiv.org/abs/2303.17766Test

المؤلفون: Shen, Yiyang, Wei, Mingqiang, Wang, Yongzhen, Fu, Xueyang, Qin, Jing

مصطلحات موضوعية: Computer Science - Computer Vision and Pattern Recognition

الوصف: Recent diffusion models have exhibited great potential in generative modeling tasks. Part of their success can be attributed to the ability of training stable on huge sets of paired synthetic data. However, adapting these models to real-world image deraining remains difficult for two aspects. First, collecting a large-scale paired real-world clean/rainy dataset is unavailable while regular conditional diffusion models heavily rely on paired data for training. Second, real-world rain usually reflects real-world scenarios with a variety of unknown rain degradation types, which poses a significant challenge for the generative modeling process. To meet these challenges, we propose RainDiff, the first real-world image deraining paradigm based on diffusion models, serving as a new standard bar for real-world image deraining. We address the first challenge by introducing a stable and non-adversarial unpaired cycle-consistent architecture that can be trained, end-to-end, with only unpaired data for supervision; and the second challenge by proposing a degradation-conditioned diffusion model that refines the desired output via a diffusive generative process conditioned by learned priors of multiple rain degradations. Extensive experiments confirm the superiority of our RainDiff over existing unpaired/semi-supervised methods and show its competitive advantages over several fully-supervised ones.
Comment: 18 pages

الوصول الحر: http://arxiv.org/abs/2301.09430Test

المؤلفون: Zhang, Bo, Hu, Mintao, Zhang, Peng, Cao, Hong, Wang, Yongzhen, Wang, Zheng, Su, Tingting

المصدر: Brazilian Journal of Medical and Biological Research. May 2013 46(5)

مصطلحات موضوعية: Primary biliary cirrhosis, Regulatory T cells, BAFF, B cells, Bezafibrate

الوصف: Primary biliary cirrhosis (PBC) is a chronic and slowly progressive cholestatic liver disease of autoimmune etiology. A number of questions regarding its etiology are unclear. CD4+CD25+ regulatory T cells (Tregs) play a critical role in self-tolerance and, for unknown reasons, their relative number is reduced in PBC patients. B-cell-activating factor (BAFF) is a key survival factor during B-cell maturation and its concentration is increased in peripheral blood of PBC patients. It has been reported that activated B cells inhibit Treg cell proliferation and there are no BAFF receptors on Tregs. Therefore, we speculated that excessive BAFF may result in Treg reduction via B cells. To prove our hypothesis, we isolated Tregs and B cells from PBC and healthy donors. BAFF and IgM concentrations were then analyzed by ELISA and CD40, CD80, CD86, IL-10, and TGF-β expression in B cells and Tregs were measured by flow cytometry. BAFF up-regulated CD40, CD80, CD86, and IgM expression in B cells. However, BAFF had no direct effect on Treg cell apoptosis and cytokine secretion. Nonetheless, we observed that BAFF-activated B cells could induce Treg cell apoptosis and reduce IL-10 and TGF-β expression. We also showed that BAFF-activated CD4+ T cells had no effect on Treg apoptosis. Furthermore, we verified that bezafibrate, a hypolipidemic drug, can inhibit BAFF-induced Treg cell apoptosis. In conclusion, BAFF promotes Treg cell apoptosis and inhibits cytokine production by activating B cells in PBC patients. The results of this study suggest that inhibition of BAFF activation is a strategy for PBC treatment.

وصف الملف: text/html

الوصول الحر: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2013000500433Test

المؤلفون: Hu, Zhiheng, Wang, Yongzhen, Li, Peng, Qin, Jie, Xie, Haoran, Wei, Mingqiang

مصطلحات موضوعية: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence

الوصف: Small targets are often submerged in cluttered backgrounds of infrared images. Conventional detectors tend to generate false alarms, while CNN-based detectors lose small targets in deep layers. To this end, we propose iSmallNet, a multi-stream densely nested network with label decoupling for infrared small object detection. On the one hand, to fully exploit the shape information of small targets, we decouple the original labeled ground-truth (GT) map into an interior map and a boundary one. The GT map, in collaboration with the two additional maps, tackles the unbalanced distribution of small object boundaries. On the other hand, two key modules are delicately designed and incorporated into the proposed network to boost the overall performance. First, to maintain small targets in deep layers, we develop a multi-scale nested interaction module to explore a wide range of context information. Second, we develop an interior-boundary fusion module to integrate multi-granularity information. Experiments on NUAA-SIRST and NUDT-SIRST clearly show the superiority of iSmallNet over 11 state-of-the-art detectors.

الوصول الحر: http://arxiv.org/abs/2210.16561Test

المؤلفون: Tong, Ming, Wang, Yongzhen, Cui, Peng, Yan, Xuefeng, Wei, Mingqiang

مصطلحات موضوعية: Computer Science - Computer Vision and Pattern Recognition

الوصف: Image dehazing is fundamental yet not well-solved in computer vision. Most cutting-edge models are trained in synthetic data, leading to the poor performance on real-world hazy scenarios. Besides, they commonly give deterministic dehazed images while neglecting to mine their uncertainty. To bridge the domain gap and enhance the dehazing performance, we propose a novel semi-supervised uncertainty-aware transformer network, called Semi-UFormer. Semi-UFormer can well leverage both the real-world hazy images and their uncertainty guidance information. Specifically, Semi-UFormer builds itself on the knowledge distillation framework. Such teacher-student networks effectively absorb real-world haze information for quality dehazing. Furthermore, an uncertainty estimation block is introduced into the model to estimate the pixel uncertainty representations, which is then used as a guidance signal to help the student network produce haze-free images more accurately. Extensive experiments demonstrate that Semi-UFormer generalizes well from synthetic to real-world images.

الوصول الحر: http://arxiv.org/abs/2210.16057Test

المؤلفون: Wang, Yongzhen, Yan, Xuefeng, Zhang, Kaiwen, Gong, Lina, Xie, Haoran, Wang, Fu Lee, Wei, Mingqiang

مصطلحات موضوعية: Computer Science - Computer Vision and Pattern Recognition

الوصف: Adverse weather conditions such as haze, rain, and snow often impair the quality of captured images, causing detection networks trained on normal images to generalize poorly in these scenarios. In this paper, we raise an intriguing question - if the combination of image restoration and object detection, can boost the performance of cutting-edge detectors in adverse weather conditions. To answer it, we propose an effective yet unified detection paradigm that bridges these two subtasks together via dynamic enhancement learning to discern objects in adverse weather conditions, called TogetherNet. Different from existing efforts that intuitively apply image dehazing/deraining as a pre-processing step, TogetherNet considers a multi-task joint learning problem. Following the joint learning scheme, clean features produced by the restoration network can be shared to learn better object detection in the detection network, thus helping TogetherNet enhance the detection capacity in adverse weather conditions. Besides the joint learning architecture, we design a new Dynamic Transformer Feature Enhancement module to improve the feature extraction and representation capabilities of TogetherNet. Extensive experiments on both synthetic and real-world datasets demonstrate that our TogetherNet outperforms the state-of-the-art detection approaches by a large margin both quantitatively and qualitatively. Source code is available at https://github.com/yzTest-wang/TogetherNet.
Comment: 12 pages, 9 figures

الوصول الحر: http://arxiv.org/abs/2209.01373Test

المؤلفون: Wang, Jie, Wang, Yongzhen, Feng, Yidan, Gong, Lina, Yan, Xuefeng, Xie, Haoran, Wang, Fu Lee, Wei, Mingqiang

مصطلحات موضوعية: Computer Science - Computer Vision and Pattern Recognition

الوصف: Image smoothing is a fundamental low-level vision task that aims to preserve salient structures of an image while removing insignificant details. Deep learning has been explored in image smoothing to deal with the complex entanglement of semantic structures and trivial details. However, current methods neglect two important facts in smoothing: 1) naive pixel-level regression supervised by the limited number of high-quality smoothing ground-truth could lead to domain shift and cause generalization problems towards real-world images; 2) texture appearance is closely related to object semantics, so that image smoothing requires awareness of semantic difference to apply adaptive smoothing strengths. To address these issues, we propose a novel Contrastive Semantic-Guided Image Smoothing Network (CSGIS-Net) that combines both contrastive prior and semantic prior to facilitate robust image smoothing. The supervision signal is augmented by leveraging undesired smoothing effects as negative teachers, and by incorporating segmentation tasks to encourage semantic distinctiveness. To realize the proposed network, we also enrich the original VOC dataset with texture enhancement and smoothing labels, namely VOC-smooth, which first bridges image smoothing and semantic segmentation. Extensive experiments demonstrate that the proposed CSGIS-Net outperforms state-of-the-art algorithms by a large margin. Code and dataset are available at https://github.com/wangjie6866/CSGIS-NetTest.

الوصول الحر: http://arxiv.org/abs/2209.00977Test