Nighttime flares, caused by complex scattering and reflections from artificial light sources, significantly degrade image quality and hinder downstream visual tasks. Existing deflare networks usually struggle to jointly capture and fuse latent spatial and frequency features. In this paper, we propose a novel Wavelet-guided and Gated-enhanced Spatial-frequency Fusion Network (WGSF-Net) for nighttime flare removal. WGSF-Net is primarily composed of two key modules: Wavelet-guided Fusion Block (WFB) and Local-Global Block (LGB). Specifically, WFB integrates a Multi-level Wavelet Enhancement Block (MWEB) and a Spatial-Frequency Fusion Network (SFFN) to effectively extract hierarchical spatial and frequency features through a coarse-to-fine strategy based on multi-level wavelet decomposition. To better suppress flare artifacts, LGB is designed to jointly capture local and global information: a Gated-Enhanced Attention Block (GEAB) selectively amplifies critical local features through a gated network and a difference network, and the subsequent SFFN performs global spatial-frequency fusion via depthwise separable convolution and partial Fourier convolution. This design enables LGB to effectively disentangle flare-corrupted regions and restore fine-grained details, making it particularly suited for challenging real-world flare scenarios. Extensive experiments on both synthetic and real datasets show that WGSF-Net achieves state-of-the-art performance in nighttime flare removal, outperforming existing methods across five evaluation metrics.

Image dehazing, a crucial task in low-level vision, supports numerous practical applications, such as autonomous driving, remote sensing, and surveillance. This paper proposes IHDCP, a novel Inverted Haze Density Correction Prior for efficient single image dehazing. It is observed that the medium transmission can be effectively modeled from the inverted haze density map using correction functions with various gamma coefficients. Based on this observation, a pixel-wise gamma correction coefficient is introduced to formulate the transmission as a function of the inverted haze density map. To estimate the transmission, IHDCP is first incorporated into the classic atmospheric scattering model (ASM), leading to a transcendental equation that is subsequently simplified to a quadratic form with a single unknown parameter using the Taylor expansion. Then, boundary constraints are designed to estimate this model parameter, and the gamma correction coefficient map is derived via the Vieta theorem. Finally, the haze-free result is recovered through ASM inversion. Experimental results on diverse synthetic and real-world datasets verify that our algorithm not only provides visually appealing dehazing performance with high computational efficiency, but also outperforms several state-of-the-art dehazing approaches in both subjective and objective evaluations. Moreover, our IHDCP generalizes well to various types of degraded scenes. Our code is available at https://github.com/TaoLi-TL/IHDCP.

Images captured under real-world nighttime haze conditions often suffer from severe degradations, including low visibility, color distortion, and reduced contrast, which not only impair visual perception but also degrade the performance of vision-based tasks. However, existing dehazing methods are mainly designed for daytime scenarios and struggle to cope with the complex illumination and scattering characteristics of nighttime hazy images. In this paper, we propose a novel Bayesian-based variational framework with fractional-order constraints for real-world nighttime image dehazing. First, a simplified physical model is constructed to characterize nighttime hazy images, accounting for haze, low-light conditions, Poisson noise, and glow degradations. An anisotropic pre-processing strategy is iteratively applied in the Lab color space to remove glow effects. Subsequently, illumination and reflectance estimation within our constructed physical model is formulated as a maximum a-posteriori (MAP) problem, which is then approximated as a unified variational optimization function. To impose prior constraints, two fractional-order terms are introduced as priors to regulate the illumination and reflectance, promoting piecewise smoothness in illumination and preserving sharp edges and fine textures in reflectance. The resulting variational model is efficiently solved using the alternating direction minimization method. Finally, the estimated illumination and reflectance are enhanced via spatial-domain gamma correction for brightness adjustment and frequency-domain processing for texture detail enhancement. Extensive experiments on real-world datasets demonstrate that the proposed framework outperforms state-of-the-art dehazing methods in both qualitative and quantitative evaluations. Besides, our algorithm generalizes effectively to both other degraded scenes and high-level vision tasks.

Images taken in low-light conditions are frequently affected by limited visibility, diminished contrast and severe noise, adversely impacting the performance of various computer vision tasks. Most variational-based Retinex decomposition methods mainly depend on integer norms to constrain the illumination and reflectance components. However, this strategy may fail to achieve the ideal Retinex decomposition. In this paper, we propose a Retinex-based variational model that incorporates flexible constraints for both illumination and reflectance. Specifically, we impose the Lpnorm constraints with varying values of p to ensure the piece-wise smoothness of the illumination and promote the presence of abundant textures in the reflectance. Moreover, we develop two effective pixel-wise weight matrices that consider variance and gradients of the input image respectively, with the objective of preserving the structural edges of the illumination and retaining more details in the reflectance. In addition, we use an L2norm to estimate the overall noise level and avoid noise amplification. Through incorporating these above constraints, our proposed variational model can obtain a structure-aware illumination and a detail-revealed reflectance. Qualitative and quantitative comparisons on real-world and synthetic datasets indicate that our approach yields results with superior visual quality and outperforms several state-of-the-art algorithms on objective metrics. Besides, our algorithm can also address similar low-level computer vision challenges, such as image dehazing and underwater image enhancement. The source code is available at https://github.com/Enping-Hu/dual weighted lp.

Deep learning based dehazing networks trained on paired synthetic data have shown impressive performance, but they struggle with significant degradation in generalization ability on real-world hazy scenes. In this paper, we propose Dehaze-RetinexGAN, a lightweight Retinex-based Generative Adversarial Network for real-world image Dehazing using unpaired data. Our Dehaze-RetinexGAN consists of two stages: self-supervised pre-training and weakly-supervised fine-tuning. During the pre-training, we reduce the image dehazing task to an illumination-reflectance decomposition task based on the duality correlation between Retinex and dehazing. Specifically, a decomposition network named DecomNet is constructed to obtain an illumination and a reflectance, simultaneously. Moreover, a self-supervised learning strategy is developed to construct the connection between the preliminary dehazed result and the input hazy image, which constrains the solution space of DecomNet and accelerates training, leading to a more realistic dehazed result. In the fine-tuning stage, we develop a dual DTCWT-based attention module and embed it into the U-Net architecture to further improve the quality of preliminary result in the frequency domain. In addition, the adversarial learning is employed to constrain the relevance between the clean image and the final dehazed result in a weakly supervised manner, which can promote more natural performance. Extensive experiments on several real-world datasets demonstrate that our proposed framework performs favorably over state-of-the-art dehazing methods in visual quality and quantitative evaluation.

Snowfall presents significant challenges for visual data processing, necessitating specialized desnowing algorithms. However, existing models often fail to generalize effectively due to their heavy reliance on synthetic datasets. Furthermore, current real-world snowfall datasets are limited in scale and lack dedicated evaluation metrics designed specifically for snowfall degradation, thus hindering the effective integration of real snowy images into model training to reduce domain gaps. To address these challenges, we first introduce RealSnow10K, a large-scale, high-quality dataset consisting of over 10,000 annotated real-world snowy images. In addition, we curate a preference dataset comprising 36,000 expert-ranked image pairs, enabling the adaptation of multimodal large language models (MLLMs) to better perceive snowy image quality through our innovative Multi-Model Preference Optimization (MMPO). Finally, we propose the SnowMaster, which employs MMPO-enhanced MLLM to perform accurate snowy image evaluation and pseudo-label filtering for semi-supervised training. Experiments demonstrate that SnowMaster delivers superior desnowing performance under real-world conditions.

Unsupervised remote sensing dehazing remains a challenging and ill-posed task due to the absence of reliable supervision signals. Existing dehazing methods with unpaired data often oversimplify haze removal as style transfer, limiting generalization in complex scenarios. Moreover, current unimodal frameworks neglect cross-modal cues that could improve contextual reasoning. To address these issues, we propose a novel cross-modal guided self-supervised dehazing framework called CLIP-HNet, which achieves multi-model feature extraction, boundary-focused reconstruction and adaptive sample filtering. Specifically, to capture global-local contextual features, a hybrid feature interaction network is designed, which bridges the feature representations of multi models with global context-aware module (GCAM) and hybrid feature fusion module (HF2 M). Then, based on the hybrid features, a boundary-aware feature reconstruction (BFRec) is proposed to further refine edge details. Furthermore, a CLIP-guided progressive information distillation scheme is presented to dynamically prioritize training samples and distill useful signals, which predicts haze concentration by CLIP and progressively increases sample difficulty during the training stage. Finally, a frequency-domain texture matching (FTM) strategy refines texture and spectral details, enhancing the model's ability to recover fine details. Experiments on synthetic and real RSIs demonstrate that the proposed CLIP-HNet surpasses state-of-the-art approaches, achieving superior visual quality and quantitative performance.

Existing nighttime flare removal work regards flare as a single degradation factor in spatial domain. However, flare in complex scenes consists of multiple flare types, and it is difficult to distinguish them from the background, leading to distorted results and incomplete perception. In this paper, we propose a self-prompt based dual-domain network named SPDDNet for nighttime flare removal, which encodes the data distributions of different flare types to generate prompt features and facilitates the interaction of the prompt features with the decoder to guide network for flare removal. In addition, we introduce Fast Fourier Transform and parallel attention in traditional convolutional neural network that is designed to extract global frequency features and location-dependent local information to accurately perceive the flare region. Finally, to adequately integrate spatial details , contextual information, prompt and image features, we propose a feature fusion module that generates a set of learned dynamic weights to adaptively guide the information fusion across channels. Extensive experiments on real-world and synthetic datasets strongly demonstrate the effectiveness of our proposed SPDDNet and its superior performance compared to state-of-the-art methods. Moreover, as an essential pre-processing step, the potential advantages of our method for other computer vision applications, including object detection and semantic segmentation, are demonstrated.

Images captured under haze weather conditions usually suffer from visual quality degradations, such as blurred details, faded colors, and decreased saturation. Existing physicsbased dehazing methods mainly have two drawbacks: 1) the atmospheric light is treated as a constant for the entire image, and 2) pixel-or patch-based strategies are employed to estimate the model parameters, resulting in inaccurate haze density estimations. Therefore, these methods may lead to over-dehazing or under-dehazing due to insufficient utilization of features from regions with similar haze densities. To address these issues, a novel single image dehazing framework based on fuzzy region segmentation and haze density decomposition is proposed. Specifically, a region-based physical model that considers the non-uniform atmospheric light is first constructed based on the classic atmospheric scattering model. Then, a fuzzy segmentation algorithm is improved to divide the input hazy image into several separate regions. Subsequently, we formulate a simple linear relationship between the atmospheric light and brightness to estimate region-based atmospheric light. On the other hand, we develop a novel haze density decomposition algorithm based on boundary constraints to separate the atmospheric veil into two components: thin part and dense part. Three haze-related features, contrast, gradient and clarity, are extracted from the input hazy image to construct weight maps and a multi-scale fusion is further exploited to combine weight maps and boundary veils to acquire the refined atmospheric veil. Finally, the model inversion is performed to acquire the haze-free result. Experiments on six diverse hazy datasets demonstrate that the proposed algorithm outperforms several state-of-the-art dehazing methods in both visual quality and objective evaluation.

The visibility of images plays a crucial role in Intelligent Transportation Systems (ITS). However, images captured under hazy environments can degrade visual quality, significantly reducing the working performance of ITS. Although existing dehazing methods have achieved remarkable performance for daytime hazy images, they struggle to overcome the unique degradations under nighttime haze conditions such as glows, weak illumination, hidden noise, and color distortions. To simultaneously address these degradations, we propose VNDHR, a novel Variational Nighttime Dehazing framework using Hybrid Regularization focusing on enhancing the perceptual visibility of nighttime hazy scenarios. Specifically, a new physical model that accounts for multiple degradations under nighttime haze conditions is first constructed. Then, a novel hybrid variational model comprising an$\ell _{p}$norm, a weighted$\ell _{2}$norm, and a total variation regularization is developed to obtain a structure-aware illumination and a noise-free reflectance, simultaneously. To remove the nonhomogeneous haze in the illumination, we employ the dark channel prior to estimate parameters in each grid patch. Furthermore, a simple but effective nonlinear stretching function is designed to enhance the texture in the decomposed reflectance component. Finally, the dehazed illumination and the stretched reflectance are combined to generate a haze-free result. Experiments performed on synthetic and real-world nighttime hazy images prove that our VNDHR framework achieves state-of-the-art dehazing performance, providing results with clear details and less noise. Besides, our VNDHR can also handle various types of degraded images well, such as low-light images, daytime hazy images, sandstorm images, and underwater images.

The core of self-supervised point cloud learning lies in setting up appropriate pretext tasks, to construct a pre-training framework that enables the encoder to perceive 3D objects effectively. In this article, we integrate two prevalent methods, masked point modeling (MPM) and 3D-to-2D generation, as pretext tasks within a pre-training framework. We leverage the spatial awareness and precise supervision offered by these two methods to address their respective limitations: ambiguous supervision signals and insensitivity to geometric information. Specifically, the proposed framework, abbreviated as PointCG, consists of a Hidden Point Completion (HPC) module and an Arbitrary-view Image Generation (AIG) module. We first capture visible points from arbitrary views as inputs by removing hidden points. Then, HPC extracts representations of the inputs with an encoder and completes the entire shape with a decoder, while AIG is used to generate rendered images based on the visible points' representations. Extensive experiments demonstrate the superiority of the proposed method over the baselines in various downstream tasks. Our code will be made available upon acceptance.

Snow removal causes challenges due to its characteristic of complex degradations. To this end, targeted treatment of multi-scale snow degradations is critical for the network to learn effective snow removal. In order to handle the diverse scenes, we propose a multi-scale projection transformer (MSP-Former), which understands and covers a variety of snow degradation features in a multi-path manner, and integrates comprehensive scene context information for clean reconstruction via self-attention operation. For the local details of various snow degradations, the local capture module is introduced in parallel to assist in the rebuilding of a clean image. Such design achieves the SOTA performance on three desnowing benchmark datasets while costing the low parameters and computational complexity, providing a guarantee of practicality.

Varicolored haze caused by chromatic casts poses haze removal and depth estimation challenges. Recent learning-based depth estimation methods are mainly targeted at dehazing first and estimating depth subsequently from haze-free scenes. This way, the inner connections between colored haze and scene depth are lost. In this paper, we propose a real-time transformer for simultaneous single image Depth Estimation and Haze Removal (DEHRFormer). DEHRFormer consists of a single encoder and two task-specific decoders. The transformer decoders with learnable queries are designed to decode coupling features from the task-agnostic encoder and project them into clean image and depth map, respectively. In addition, we introduce a novel learning paradigm that utilizes contrastive learning and domain consistency learning to tackle weak-generalization problem for real-world dehazing, while predicting the same depth map from the same scene with varicolored haze. Experiments demonstrate that DEHRFormer achieves significant performance improvement across diverse varicolored haze scenes over previous depth estimation networks and dehazing approaches.

Despite recent advancements in unified adverse weather removal methods, there remains a significant challenge of achieving realistic fine-grained texture and reliable background reconstruction to mitigate serious distortions.Inspired by recent advancements in codebook and vector quantization (VQ) techniques, we present a novel Adverse Weather Removal network with Codebook Priors (AWRCP) to address the problem of unified adverse weather removal. AWRCP leverages high-quality codebook priors derived from undistorted images to recover vivid texture details and faithful background structures. However, simply utilizing high-quality features from the codebook does not guarantee good results in terms of fine-grained details and structural fidelity. Therefore, we develop a deformable cross-attention with sparse sampling mechanism for flexible perform feature interaction between degraded features and high-quality features from codebook priors. In order to effectively incorporate high-quality texture features while maintaining the realism of the details generated by codebook priors, we propose a hierarchical texture warping head that gradually fuses hierarchical codebook prior features into high-resolution features at final restoring stage.With the utilization of the VQ codebook as a feature dictionary of high quality and the proposed designs, AWRCP can largely improve the restored quality of texture details, achieving the state-of-the-art performance across multiple adverse weather removal benchmark.

Underwater images typically experience mixed degradations of brightness and structure caused by the absorption and scattering of light by suspended particles. To address this issue, we propose a Real-time Spatial and Frequency Domains Modulation Network (RSFDM-Net) for the efficient enhancement of colors and details in underwater images. Specifically, our proposed conditional network is designed with Adaptive Fourier Gating Mechanism (AFGM) and Multiscale Convolutional Attention Module (MCAM) to generate vectors carrying low-frequency background information and high-frequency detail features, which effectively promote the network to model global background information and local texture details. To more precisely correct the color cast and low saturation of the image, we introduce a Three-branch Feature Extraction (TFE) block in the primary net that processes images pixel by pixel to integrate the color information extended by the same channel (R, G, or B). This block consists of three small branches, each of which has its own weights. Extensive experiments demonstrate that our network significantly outperforms over state-of-the-art methods in both visual quality and quantitative metrics.

Removing snow from a single image poses a significant challenge within the image restoration domain, as snowfall's effects are in various scales and forms. Existing methods have tried to tackle this issue by using multi-scale approaches, but their reliance on targeted design for handling each single-scale feature has resulted in unsatisfactory performance. This is primarily due to a lack of cross-scale knowledge, making it difficult to effectively handle degradations. To this end, we propose a novel approach, CPLFormer, which uses snow prototypes to own comprehensive clean scene understanding through learning from cross-scale features, outperforming convolutional network and vanilla transformer-based solutions. CPLFormer has several advantages: firstly, learnable snow prototypes learn global context information from multiple scales to uncover hidden clean cues; secondly, prototypes can propagate cross-scale information to each patch through cross-attention to assist with clean patch reconstruction; thirdly, CPLFormer surpasses advanced state-of-the-art desnowing networks and the prevalent universal image restoration transformers on six synthetic and real-world benchmark tests.

Single-image snow removal aims to restore clean images from heterogeneous and irregular snow degradations. Recent methods utilize neural networks to remove various degradations directly. However, these approaches suffer from the limited ability to flexibly perceive complicated snow degradation patterns and insufficient representation of background structure information. To further improve the performance and generalization ability of snow removal, this paper aims to develop a novel and efficient paradigm from the perspective of degradation perceiving and background modeling.

Nighttime image dehazing is a challenging task due to the presence of multiple types of adverse degrading effects including glow, haze, blur, noise, color distortion, and so on. However, most previous studies mainly focus on daytime image dehazing or partial degradations presented in nighttime hazy scenes, which may lead to unsatisfactory restoration results. In this paper, we propose an end-to-end transformer-based framework for nighttime haze removal, called NightHazeFormer. Our proposed approach consists of two stages: supervised pre-training and semi-supervised fine-tuning. During the pre-training stage, we introduce two powerful priors into the transformer decoder to generate the non-learnable prior queries, which guide the model to extract specific degradations. For the fine-tuning, we combine the generated pseudo ground truths with input real-world nighttime hazy images as paired images and feed into the synthetic domain to fine-tune the pre-trained model. This semi-supervised fine-tuning paradigm helps improve the generalization to real domain. In addition, we also propose a large-scale synthetic dataset called UNREAL-NH, to simulate the real-world nighttime haze scenarios comprehensively. Extensive experiments on several synthetic and real-world datasets demonstrate the superiority of our NightHazeFormer over state-of-the-art nighttime haze removal methods in terms of both visually and quantitatively.

Video restoration networks aim to restore high-quality frame sequences from degraded ones. However, traditional video restoration methods heavily rely on temporal modeling operators or optical flow estimation, which limits their versatility. The aim of this work is to present a novel approach for video restoration that eliminates inefficient temporal modeling operators and pixel-level feature alignment in the network architecture. The proposed method, Sequential Affinity Learning Network (SALN), is designed based on an affinity mechanism that establishes direct correspondences between the Query frame, degraded sequence, and restored frames in latent space. This unique perspective allows for more accurate and effective restoration of video content without relying on temporal modeling operators or optical flow estimation techniques. Moreover, we enhanced the design of the channel-wise self-attention block to improve the decoder's performance for video restoration. Our method outperformed previous state-of-the-art methods by a significant margin in several classic video tasks, including video deraining, video dehazing, and video waterdrop removal, demonstrating excellent efficiency. As a novel network that differs significantly from previous video restoration methods, SALN aims to provide innovative ideas and directions for video restoration. Our contributions include proposing a novel affinity-based approach for video restoration, enhancing the design of the channel-wise self-attention block, and achieving state-of-the-art performance on several classic video tasks.

Under the nighttime haze environment, the quality of acquired images will be deteriorated significantly owing to the influences of multiple adverse degradation factors. In this paper, we develop a multi-purpose oriented haze removal framework focusing on nighttime hazy images. First, we construct a nonlinear model based on the classic Retinex theory to formulate multiple adverse degradations of a nighttime hazy image. Then, a novel variational Retinex model is presented to simultaneously estimate a smoothed illumination component and a detail-revealed reflectance component and predict the noise map from a pre-processed nighttime hazy image in a unified manner. Specifically, an${\ell _{0}}$norm is imposed on the reflectance to reveal the structural details and we make use of$\ell _{1}$norm to constrain the piece-wise smoothness of the illumination and apply${\ell _{2}}$norm to enforce the total intensity of the noise map. Afterwards, the haze in the illumination component is removed based on prior-based dehazing method and the contrast of the reflectance component is improved in the gradient domain. Finally, we combine the dehazed illumination and the improved reflectance to generate the haze-free image. Experiments show that our proposed framework performs better than famous nighttime image dehazing methods both in visual effects and objective comparisons. In addition, the proposed framework can also be applicable to other types of degraded images.

Self-supervised learning is attracting large attention in point cloud understanding. However, exploring discriminative and transferable features still remains challenging due to their nature of irregularity. We propose a geometrically and adaptively masked auto-encoder on point clouds for self-supervised learning, termedPointGame. PointGame contains two core components: GATE and EAT. GATE stands for the geometrical and adaptive token embedding module; it not only absorbs the conventional wisdom of geometric descriptors that captures the surface shape effectively, but also exploits adaptive saliency to focus on the salient part of a point cloud. EAT stands for the external attention-based Transformer encoder with linear computational complexity, which increases the efficiency of the whole pipeline. Unlike cutting-edge unsupervised learning models, PointGame leverages geometric descriptors to perceive surface shapes and adaptively mines discriminative features from training data. PointGame showcases clear advantages over its competitors on various downstream tasks under both global and local fine-tuning strategies. The code and pre-trained models will be publicly available.

Few-shot image classification with graph neural network (GNN) is a hot topic in recent years. Most GNN-based approaches have achieved promising performance. These methods utilize node features or one-dimensional edge feature for classification ignoring rich edge featues between nodes. In this letter, we propose a novel graph neural network exploiting multi-dimensional edge features (MDE-GNN) based on edge-labeling graph neural network (EGNN) and transductive neural network for few-shot learning. Unlike previous GNN-based approaches, we utilize multi-dimensional edge features information to construct edge matrices in graph. After layers of node and edge feautres updating, we generate a similarity score matrix by the mulit-dimensional edge features through a well-designed edge aggregation module. The parameters in our network are iteratively learnt by episode training with an edge similarity loss. We apply our model to supervised few-shot image classification tasks. Compared with previous GNNs and other few-shot learning approaches, we achieve state-of-the-art performance with two benchmark datasets.