In this paper, we reveal a novel haze-specific wavelet degradation prior observed through wavelet transform analysis, which shows that haze-related information predominantly resides in low-frequency components. Exploiting this insight, we propose a novel dehazing framework, WDMamba, which decomposes the image dehazing task into two sequential stages: low-frequency restoration followed by detail enhancement. This coarse-to-fine strategy enables WDMamba to effectively capture features specific to each stage of the dehazing process, resulting in high-quality restored images. Specifically, in the low-frequency restoration stage, we integrate Mamba blocks to reconstruct global structures with linear complexity, efficiently removing overall haze and producing a coarse restored image. Thereafter, the detail enhancement stage reinstates fine-grained information that may have been overlooked during the previous phase, culminating in the final dehazed output. Furthermore, to enhance detail retention and achieve more natural dehazing, we introduce a self-guided contrastive regularization during network training. By utilizing the coarse restored output as a hard negative example, our model learns more discriminative representations, substantially boosting the overall dehazing performance. Extensive evaluations on public dehazing benchmarks demonstrate that our method surpasses state-of-the-art approaches both qualitatively and quantitatively. Code is available at https://github.com/SunJ000/WDMamba.

Recent diffusion models have demonstrated exceptional efficacy across various image restoration tasks, but still suffer from time-consuming and substantial computational resource consumption. To address these challenges, we present LPCDiff, a novel Laplacian Pyramid-based Conditional Diffusion model designed for real-scene image dehazing. LPCDiff leverages the Laplacian pyramid decomposition to decouple the input image into two components: the low-resolution low-pass image and the high-frequency residuals. These components are subsequently reconstructed through a diffusion model and a well-designed high-frequency residual recovery module. With such a strategy, LPCDiff can substantially accelerate inference speed and reduce computational costs without sacrificing image fidelity. In addition, the framework empowers the model to capture intrinsic high-frequency details and low-frequency structural information within the image, resulting in sharper and more realistic haze-free outputs. Moreover, to extract more valuable information from the limited training data, we introduce a low-frequency refinement module to further enhance the intricate details of the final dehazed images. Through extensive experimentation, our method significantly outperforms 12 state-of-the-art approaches on three real-world and one synthetic image dehazing benchmarks. Code is available athttps://github.com/yz-wang/LPCDiff.

Natural images are often degraded by complex, composite degradations such as rain, snow, and haze, which adversely impact downstream vision applications. While existing image restoration efforts have achieved notable success, they are still hindered by two critical challenges: limited generalization across dynamically varying degradation scenarios and a suboptimal balance between preserving local details and modeling global dependencies. To overcome these challenges, we propose M2Restore, a novel Mixture-of-Experts (MoE)-based Mamba-CNN fusion framework for efficient and robust all-in-one image restoration. M2Restore introduces three key contributions: First, to boost the model's generalization across diverse degradation conditions, we exploit a CLIP-guided MoE gating mechanism that fuses task-conditioned prompts with CLIP-derived semantic priors. This mechanism is further refined via cross-modal feature calibration, which enables precise expert selection for various degradation types. Second, to jointly capture global contextual dependencies and fine-grained local details, we design a dual-stream architecture that integrates the localized representational strength of CNNs with the long-range modeling efficiency of Mamba. This integration enables collaborative optimization of global semantic relationships and local structural fidelity, preserving global coherence while enhancing detail restoration. Third, we introduce an edge-aware dynamic gating mechanism that adaptively balances global modeling and local enhancement by reallocating computational attention to degradation-sensitive regions. This targeted focus leads to more efficient and precise restoration. Extensive experiments across multiple image restoration benchmarks validate the superiority of M2Restore in both visual quality and quantitative performance. Code is available at https://github.com/yz-wang/M2Restore.

Haze severely degrades the visual quality of remote sensing images and hampers the performance of road extraction, vehicle detection, and traffic flow monitoring. 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 state-of-the-art methods. Source code will be released athttps://github.com/jm-xiong/RSHazeDiff

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.

While the wisdom of training an image dehazing model on synthetic hazy data can alleviate the difficulty of collecting real-world hazy/clean image pairs, it brings the well-known domain shift problem. From a different yet new perspective, this paper explores contrastive learning with an adversarial training effort to leverage unpaired real-world hazy and clean images, thus alleviating the domain shift problem and enhancing the network's generalization ability in real-world scenarios. We propose an effective unsupervised contrastive learning paradigm for image dehazing, dubbed UCL-Dehaze. Unpaired real-world clean and hazy images are easily captured, and will serve as the important positive and negative samples respectively when training our UCL-Dehaze network. To train the network more effectively, we formulate a new self-contrastive perceptual loss function, which encourages the restored images to approach the positive samples and keep away from the negative samples in the embedding space. Besides the overall network architecture of UCL-Dehaze, adversarial training is utilized to align the distributions between the positive samples and the dehazed images. Compared with recent image dehazing works, UCL-Dehaze does not require paired data during training and utilizes unpaired positive/negative data to better enhance the dehazing performance. We conduct comprehensive experiments to evaluate our UCL-Dehaze and demonstrate its superiority over the state-of-the-arts, even only 1,800 unpaired real-world images are used to train our network. Source code is publicly available at https://github.com/yz-wang/UCL-Dehaze.

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.

Haze severely degrades the visibility of scene objects and deteriorates the performance of autonomous driving, traffic monitoring, and other vision-based intelligent transportation systems. As a potential remedy, we propose a novel unified Transformer with semantically contrastive learning for image dehazing, dubbed USCFormer. USCFormer has three key contributions. First, USCFormer absorbs the respective strengths of CNN and Transformer by incorporating them into a unified Transformer format. Thus, it allows the simultaneous capture of global-local dependency features for better image dehazing. Second, by casting clean/hazy images as the positive/negative samples, the contrastive constraint encourages the restored image to be closer to the ground-truth images (positives) and away from the hazy ones (negatives). Third, we regard the semantic information as important prior knowledge to help USCFormer mitigate the effects of haze on the scene and preserve image details and colors by leveraging intra-object semantic correlation. Experiments on synthetic datasets and real-world hazy photos fully validate the superiority of USCFormer in both perceptual quality assessment and subjective evaluation. Code is available athttps://github.com/yz-wang/USCFormer.

Abstract Real‐world rain is a mixture of rain streaks and rainy haze. However, current efforts formulate image rain streaks removal and rainy haze removal as separated models, worsening the loss of image details. This paper attempts to solve the mixture of rain removal problem in a single model by estimating the scene depths of images. To this end, we propose a novel SEMI‐ supervised M ixture O f rain RE moval G enerative A dversarial N etwork (Semi‐MoreGAN). Unlike most of existing methods, Semi‐MoreGAN is a joint learning paradigm of mixture of rain removal and depth estimation; and it effectively integrates the image features with the depth information for better rain removal. Furthermore, it leverages unpaired real‐world rainy and clean images to bridge the gap between synthetic and real‐world rain. Extensive experiments show clear improvements of our approach over twenty representative state‐of‐the‐arts on both synthetic and real‐world rainy images. Source code is available at https://github.com/syy-whu/Semi-MoreGAN .

Abstract 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-Net .

Abstract 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/yz-wang/TogetherNet .

Urban trees are often densely planted along the two sides of a street. When observing these trees from a fixed view, they are inevitably occluded with each other and the passing vehicles. The high density and occlusion of urban tree scenes significantly degrade the performance of object detectors. This paper raises an intriguing learning-related question – if a module is developed to enable the network to adaptively cope with occluded and un-occluded regions while enhancing its feature extraction capabilities, can the performance of a cutting-edge detection model be improved? To answer it, a lightweight yet effective object detection network is proposed for discerning occluded and dense urban trees, called OD-UTDNet. The main contribution is a newly-designed Dilated Attention Cross Stage Partial (DACSP) module. DACSP can expand the fields-of-view of OD-UTDNet for paying more attention to the un-occluded region, while enhancing the network’s feature extraction ability in the occluded region. This work further explores both the self-calibrated convolution module and GFocal loss, which enhance the OD-UTDNet’s ability to resolve the challenging problem of high densities and occlusions. Finally, to facilitate the detection task of urban trees, a high-quality urban tree detection dataset is established, named UTD; to our knowledge, this is the first time. Extensive experiments show clear improvements of the proposed OD-UTDNet over twelve representative object detectors on UTD. The code and dataset are available at https://github.com/yzwang/OD-UTDNet.

Image dehazing is a common operation in autonomous driving, traffic monitoring and surveillance. Learning-based image dehazing has achieved excellent performance recently. However, it is nearly impossible to capture pairs of hazy/clean images from the real world to train an image dehazing network. Most of existing dehazing models that are learnt from synthetically generated hazy images generalize poorly on real-world hazy scenarios due to the obvious domain shift. To deal with this unpaired problem arisen by real-world hazy images, we present Cycle Spectral Normalized Soft likelihood estimation Patch Generative Adversarial Network (Cycle-SNSPGAN) for image dehazing. Cycle-SNSPGAN is an unsupervised dehazing framework to boost the generalization ability on real-world hazy images. To leverage unpaired samples of real-world hazy images without relying on their clean counterparts, we design an SN-Soft-Patch GAN and exploit a new cyclic self-perceptual loss which avoids using the ground-truth image to compute the perceptual similarity. Moreover, a significant color loss is adopted to brighten the dehazed images as human expects. Both visual and numerical results show clear improvements of the proposed Cycle-SNSPGAN over state-of-the-arts in terms of hazy-robustness and image detail recovery, with even only a small dataset training our Cycle-SNSPGAN. Code has been available athttps://github.com/yz-wang/Cycle-SNSPGAN.