Recently, there has been rapid and significant progress on image dehazing. Many deep learning based methods have shown their superb performance in handling homogeneous dehazing problems. However, we observe that even if a carefully designed convolutional neural network (CNN) can perform well on large-scaled dehazing benchmarks, the network usually fails on the non-homogeneous dehazing datasets introduced by NTIRE challenges. The reasons are mainly in two folds. Firstly, due to its non-homogeneous nature, the non-uniformly distributed haze is harder to be removed than the homogeneous haze. Secondly, the research challenge only provides limited data (there are only 25 training pairs in NH-Haze 2021 dataset). Thus, learning the mapping from the domain of hazy images to that of clear ones based on very limited data is extremely hard. To this end, we propose a simple but effective approach for non-homogeneous dehazing via ensemble learning. To be specific, we introduce a two-branch neural network to separately deal with the aforementioned problems and then map their distinct features by a learnable fusion tail. We show extensive experimental results to illustrate the effectiveness of our proposed method.
In recent years, single image dehazing models (SIDM) based on atmospheric scattering model (ASM) have achieved remarkable results. However, it is noted that ASM-based SIDM degrades its performance in dehazing real world hazy images due to the limited modelling ability of ASM where the atmospheric light factor (ALF) and the angular scattering coefficient (ASC) are assumed as constants for one image. Obviously, the hazy images taken in real world cannot always satisfy this assumption. Such generating modelling mismatch between the real-world images and ASM sets up the upper bound of trained ASM-…
Universitatea Politehnica Timisoara（蒂米什瓦拉理工大学）；ETH Zurich（苏黎世联邦理工学院）；University of Girona（赫罗纳大学）
Image dehazing is an ill-posed problem that has been extensively studied in the recent years. The objective performance evaluation of the dehazing methods is one of the major obstacles due to the lacking of a reference dataset. While the synthetic datasets have shown important limitations, the few realistic datasets introduced recently assume homogeneous haze over the entire scene. Since in many real cases haze is not uniformly distributed we introduce NH-HAZE, a non-homogeneous realistic dataset with pairs of real hazy and corresponding haze-free images. This is the first non-homogeneous imag…
Addressing Domain Discrepancy: A Dual-branch Collaborative Model to Unsupervised Dehazing
College of Computer Science and Engineering, Chongqing University of Technology（重庆理工大学计算机科学与工程学院）；Industrial Training Center, Guangdong Polytechnic Normal University（广东技术师范大学工业训练中心）
Multi-Branch Non-Homogeneous Image Dehazing via Concentration Partitioning and Image Fusion
School of Software and Internet of Things Engineering, Zhejiang Gongshang University（软件与物联网工程学院，浙江工商大学）；School of Computer Science and Technology, Tiangong University（计算机科学与技术学院，天工大学）
Shandong Normal University（山东师范大学）；Xidian University（西安电子科技大学）；Northwestern University（西北大学）；Chongqing University of Posts and Telecommunications（重庆邮电大学）
Single image dehazing is a prerequisite which affects the performance of many computer vision tasks and has attracted increasing attention in recent years. However, most existing dehazing methods emphasize more on haze removal but less on the detail recovery of the dehazed images. In this paper, we propose a single image dehazing method with an independent Detail Recovery Network (DRN), which considers capturing the details from the input image over a separate network and then integrates them into a coarse dehazed image. The overall network consists of two independent networks, named DRN and t…
Universitatea Politehnica Timisoara（蒂米什瓦拉理工大学）；University of Girona（赫罗纳大学）；ETH Zurich（苏黎世联邦理工学院）
Single image dehazing is an ill-posed problem that has recently drawn important attention. Despite the significant increase in interest shown for dehazing over the past few years, the validation of the dehazing methods remains largely unsatisfactory, due to the lack of pairs of real hazy and corresponding haze-free reference images. To address this limitation, we introduce Dense-Haze - a novel dehazing dataset. Characterized by dense and homogeneous hazy scenes, Dense-Haze contains 33 pairs of real hazy and corresponding haze-free images of various outdoor scenes. The hazy scenes have been rec…
Indian Institute of Technology (ISM), Dhanbad（印度理工学院（ISM）丹巴德分校）；UiT The Arctic University of Norway（挪威北极大学）
Learning to dehaze single hazy images, especially using a small training dataset is quite challenging. We propose a novel generative adversarial network architecture for this problem, namely back projected pyramid network (BPPNet), that gives good performance for a variety of challenging haze conditions, including dense haze and inhomogeneous haze. Our architecture incorporates learning of multiple levels of complexities while retaining spatial context through iterative blocks of UNets and structural information of multiple scales through a novel pyramidal convolution block. These blocks toget…
Recently, CNN based end-to-end deep learning methods achieve superiority in Image Dehazing but they tend to fail drastically in Non-homogeneous dehazing. Apart from that, existing popular Multi-scale approaches are runtime intensive and memory inefficient. In this context, we proposed a fast Deep Multi-patch Hierarchical Network to restore Non-homogeneous hazed images by aggregating features from multiple image patches from different spatial sections of the hazed image with fewer number of network parameters. Our proposed method is quite robust for different environments with various density o…
State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院虚拟现实技术与系统国家重点实验室）；Key Laboratory of Solar Activity, National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台太阳活动重点实验室）；Peng Cheng Laboratory（鹏城实验室）
Conventional CNNs-based dehazing models suffer from two essential issues: the dehazing framework (limited in interpretability) and the convolution layers (content-independent and ineffective to learn long-range dependency information). In this paper, firstly, we propose a new complementary feature enhanced framework, in which the complementary features are learned by several complementary subtasks and then together serve to boost the performance of the primary task. One of the prominent advantages of the new framework is that the purposively chosen complementary tasks can focus on learning wea…
MB-TaylorFormer: Multi-branch Efficient Transformer Expanded by Taylor Formula for Image Dehazing
Sun Yat-sen University（中山大学）；Australian National University（澳大利亚国立大学）；Guangdong Provincial Key Laboratory of Robotics and Digital Intelligent Manufacturing Technology（广东省机器人与数字智能制造技术重点实验室）
This paper proposes an end-to-end Efficient Re-parameterizationResidual Attention Network(ERRA-Net) to directly restore the nonhomogeneous hazy image. The contribution of this paper mainly has the following three aspects: 1) A novel Multi-branch Attention (MA) block. The spatial attention mechanism better reconstructs high-frequency features, and the channel attention mechanism treats the features of different channels differently. Multi-branch structure dramatically improves the representation ability of the model and can be changed into a single path structure after re-parameterization to sp…
Jiangxi Normal University（江西师范大学）；East China Normal University（华东师范大学）
Single image dehazing is a challenging ill-posed restoration problem. Various prior-based and learning-based methods have been proposed. Most of them follow a classic atmospheric scattering model which is an elegant simplified physical model based on the assumption of single-scattering and homogeneous atmospheric medium. The formulation of haze in realistic environment is more complicated. In this paper, we propose to take its essential mechanism as "black box", and focus on learning an input-adaptive trainable end-to-end dehazing model. An U-Net like encoder-decoder deep network via progressi…
Shandong Normal University（山东师范大学）；Nanjing University of Science and Technology（南京理工大学）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）
Currently, most single image dehazing models cannot run an ultra-high-resolution (UHD) image with a single GPU shader in real-time. To address the problem, we introduce the principle of infinite approximation of Taylor's theorem with the Laplace pyramid pattern to build a model which is capable of handling 4K hazy images in real-time. The N branch networks of the pyramid network correspond to the N constraint terms in Taylor's theorem. Low-order polynomials reconstruct the low-frequency information of the image (e.g. color, illumination). High-order polynomials regress the high-frequency infor…
Peking University（北京大学）；Peng Cheng Laboratory（鹏城实验室）；South China University of Technology（华南理工大学）
In recent years, single image dehazing deep models based on Atmospheric Scattering Model (ASM) have achieved remarkable results. But the dehazing outputs of those models suffer from color shift. Analyzing the ASM model shows that the atmospheric light factor (ALF) is set as a scalar which indicates ALF is constant for whole image. However, for images taken in real-world, the illumination is not uniformly distributed over whole image which brings model mismatch and possibly results in color shift of the deep models using ASM. Bearing this in mind, in this study, first, a new non-homogeneous atm…
Real-World Remote Sensing Image Dehazing: Benchmark and Baseline
MOE Key Laboratory of ICSP, IMIS Laboratory of Anhui, Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Zenmorn-AHU AI Joint Laboratory, School of Computer Science and Technology, Anhui University（教育部ICSP重点实验室、安徽IMIS实验室、安徽省多模态认知计算重点实验室、Zenmorn-AHU人工智能联合实验室、计算机科学与技术学院、安徽大学）
Efficient Dual-domain Image Dehazing with Haze Prior Perception
Institute of Intelligent Information Processing, Guangdong Provincial Key Laboratory of Intelligent Information Processing, Shenzhen Key Laboratory of Modern Communications and Information Processing, Shenzhen University（智能信息处理研究院）；College of Engineering and Computer Science, Australian National University（工程与计算机科学学院）
Accurate and lightweight dehazing via multi-receptive-field non-local network and novel contrastive regularization
Huanjiang Laboratory, Zhuji, P.R. China and School of Aeronautics and Astronautics, Zhejiang University, Hangzhou, P.R.China（湖南江实验室，浙江诸暨，中国 和 浙江大学航空宇航学院，杭州，中国）；School of Aeronautics and Astronautics, Zhejiang University, Hangzhou, P.R.China（浙江大学航空宇航学院，杭州，中国）；Electrical and Computer Engineering Department, Hellenic Mediterranean University, Heraklion, Crete, Greece（希腊地中海大学电气与计算机工程系，赫拉克利翁，克里特，希腊）
Haze can degrade the visibility and the image quality drastically, thus degrading the performance of computer vision tasks such as object detection. Single image dehazing is a challenging and ill-posed problem, despite being widely studied. Most existing methods assume that haze has a uniform/homogeneous distribution and haze can have a single color, i.e. grayish white color similar to smoke, while in reality haze can be distributed non-uniformly with different patterns and colors. In this paper, we focus on haze created by sunlight as it is one of the most prevalent type of haze in the wild. …
Laplace-Mamba: Laplace Frequency Prior-Guided Mamba-CNN Fusion Network for Image Dehazing
School of Computer Science and Technology, Anhui University of Technology（安徽理工大学计算机科学与技术学院）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；College of Artificial Intelligence, Taiyuan University of Technology（太原科技大学人工智能学院）
DPSF-Net: A Dual-Prior Spatial-Frequency Network for Real-World Remote Sensing Image Dehazing
School of Software Engineering, Jinling Institute of Technology（金陵科技学院软件工程学院）；School of Information Engineering, Yancheng Institute of Technology（盐城工学院信息工程学院）
National Chung Hsing University（中兴大学）；National Taichung University of Science and Technology（台中科技大学）
Image restoration is a challenging and ill-posed problem which also has been a long-standing issue. However, most of learning based restoration methods are proposed to target one degradation type which means they are lack of generalization. In this paper, we proposed a multi-branch restoration model inspired from the Human Visual System (i.e., Retinal Ganglion Cells) which can achieve multiple restoration tasks in a general framework. The experiments show that the proposed multi-branch architecture, called CMFNet, has competitive performance results on four datasets, including image dehazing, …
DA-IICT（达伊尔-阿尼尔·阿加瓦尔信息与通信技术学院）；Adani Institute of Infrastructure Engineering（阿达尼基础设施工程学院）
Image dehazing has become one of the crucial preprocessing steps for any computer vision task. Most of the dehazing methods try to estimate the transmission map along with the atmospheric light to get the dehazed image in the image domain. In this paper, we propose a novel end-to-end architecture that directly estimates dehazed image in Krawtchouk transform domain. For this a customized Krawtchouk Convolution Layer (KCL) in the architecture is added. KCL is constructed using Krawtchouk basis functions which converts the image from the spatial domain to the Krawtchouk transform domain. Another …
University of Electronic Science and Technology of China（电子科技大学）
Due to the lack of natural scene and haze prior information, it is greatly challenging to completely remove the haze from a single image without distorting its visual content. Fortunately, the real-world haze usually presents non-homogeneous distribution, which provides us with many valuable clues in partial well-preserved regions. In this paper, we propose a Non-Homogeneous Haze Removal Network (NHRN) via artificial scene prior and bidimensional graph reasoning. Firstly, we employ the gamma correction iteratively to simulate artificial multiple shots under different exposure conditions, whose…
Hazy images are often subject to color distortion, blurring, and other visible quality degradation. Some existing CNN-based methods have great performance on removing homogeneous haze, but they are not robust in non-homogeneous case. The reasons are mainly in two folds. Firstly, due to the complicated haze distribution, texture details are easy to be lost during the dehazing process. Secondly, since the training pairs are hard to be collected, training on limited data can easily lead to over-fitting problem. To tackle these two issues, we introduce a novel dehazing network using 2D discrete wa…
Multi-Knowledge-oriented Nighttime Haze Imaging Enhancer for Vision-driven Intelligent Systems
School of Computer Science and Engineering（计算机科学与工程学院）；University of Electronic Science and Technology of China（电子科技大学）；Department of Logistics and Maritime Studies（物流与海事研究系）；The Hong Kong Polytechnic University（香港理工大学）；Chengdu Union Big Data Tech Inc.（成都联合大数据科技有限公司）
Beijing Jiaotong University（北京交通大学）；University of Electronic Science and Technology of China（电子科技大学）
Recently, convolutional neural network (CNN) has demonstrated significant success for image restoration (IR) tasks (e.g., image super-resolution, image deblurring, rain streak removal, and dehazing). However, existing CNN based models are commonly implemented as a single-path stream to enrich feature representations from low-quality (LQ) input space for final predictions, which fail to fully incorporate preceding low-level contexts into later high-level features within networks, thereby producing inferior results. In this paper, we present a deep interleaved network (DIN) that learns how infor…
Nanjing University of Science and Technology（南京理工大学）；NVIDIA（英伟达）；City University of Hong Kong（香港城市大学）；Dalian University of Technology（大连理工大学）；SenseTime Research（商汤科技研究院）；Tencent Youtu Lab（腾讯优图实验室）；UC Merced（加州大学默塞德分校）
In this paper, we propose a general dual convolutional neural network (DualCNN) for low-level vision problems, e.g., super-resolution, edge-preserving filtering, deraining and dehazing. These problems usually involve the estimation of two components of the target signals: structures and details. Motivated by this, our proposed DualCNN consists of two parallel branches, which respectively recovers the structures and details in an end-to-end manner. The recovered structures and details can generate the target signals according to the formation model for each particular application. The DualCNN i…
MB-TaylorFormer V2: Improved Multi-branch Linear Transformer Expanded by Taylor Formula for Image Restoration
School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区智能系统工程学院）；Guangdong Provincial Key Laboratory of Fire Science and Technology（广东省火灾科学与技术重点实验室）；School of Computer Science and Technology, Shenzhen Campus of Harbin Institute of Technology（哈尔滨工业大学深圳校区计算机科学与技术学院）；School of Engineering and Computer Science, Australian National University（澳大利亚国立大学工程与计算机科学学院）；Hong Kong University of Science and Technology（香港科技大学）
CLIP-DQA: Blindly Evaluating Dehazed Images from Global and Local Perspectives Using CLIP
School of Computer Science and Informatics, Cardiff University（卡迪夫大学计算机科学与信息学院）；Alibaba Group（阿里巴巴集团）；School of Biomedical Engineering, Shenzhen University（深圳大学生物医学工程学院）
Ultra-High-Definition Image Restoration: New Benchmarks and A Dual Interaction Prior-Driven Solution
Dalian University of Technology（大连理工大学）；Centre for Advances in Reliability and Safety（可靠性与安全先进中心）；Nanjing University of Science and Technology（南京理工大学）；Yale University（耶鲁大学）；Shenzhen Campus of Sun Yat-Sen University（中山大学深圳校区）；Key Laboratory for Computational Mathematics and Data Intelligence of Liaoning Province（辽宁省计算数学与数据智能重点实验室）
