Advancements in Remote Sensing Image Dehazing: Introducing URA-Net with Multi-Scale Dense Feature Fusion Clusters and Gated Jump Connection

The degradation of optical remote sensing images due to atmospheric haze poses a significant obstacle,profoundly impeding their effective utilization across various domains.Dehazing methodologies have emerged as pivotal components of image preprocessing,fostering an improvement in the quality of remote sensing imagery.This enhancement renders remote sensing data more indispensable,thereby enhancing the accuracy of target iden-tification.Conventional defogging techniques based on simplistic atmospheric degradation models have proven inadequate for mitigating non-uniform haze within remotely sensed images.In response to this challenge,a novel UNet Residual Attention Network(URA-Net)is proposed.This paradigmatic approach materializes as an end-to-end convolutional neural network distinguished by its utilization of multi-scale dense feature fusion clusters and gated jump connections.The essence of our methodology lies in local feature fusion within dense residual clusters,enabling the extraction of pertinent features from both preceding and current local data,depending on contextual demands.The intelligently orchestrated gated structures facilitate the propagation of these features to the decoder,resulting in superior outcomes in haze removal.Empirical validation through a plethora of experiments substantiates the efficacy of URA-Net,demonstrating its superior performance compared to existing methods when applied to established datasets for remote sensing image defogging.On the RICE-1 dataset,URA-Net achieves a Peak Signal-to-Noise Ratio(PSNR)of 29.07 dB,surpassing the Dark Channel Prior(DCP)by 11.17 dB,the All-in-One Network for Dehazing(AOD)by 7.82 dB,the Optimal Transmission Map and Adaptive Atmospheric Light For Dehazing(OTM-AAL)by 5.37 dB,the Unsupervised Single Image Dehazing(USID)by 8.0 dB,and the Superpixel-based Remote Sensing Image Dehazing(SRD)by 8.5 dB.Particularly noteworthy,on the SateHaze1k dataset,URA-Net attains preeminence in overall performance,yielding defogged images characterized by consistent visual quality.This underscores the contribution of the research to the advancement of remote sensing technology,providing a robust and efficient solution for alleviating the adverse effects of haze on image quality.

Atrous Convolution-Based Residual Deep CNN for Image Dehazing with Spider Monkey-Particle Swarm Optimization

Image dehazing is a rapidly progressing research concept to enhance image contrast and resolution in computer vision applications.Owing to severe air dispersion,fog,and haze over the environment,hazy images pose specific challenges during information retrieval.With the advances in the learning theory,most of the learning-based techniques,in particular,deep neural networks are used for single-image dehazing.The existing approaches are extremely computationally complex,and the dehazed images are suffered from color distortion caused by the over-saturation and pseudo-shadow phenomenon.However,the slow convergence rate during training and haze residual is the two demerits in the conventional image dehazing networks.This article proposes a new architecture“Atrous Convolution-based Residual Deep Convolutional Neural Network(CNN)”method with hybrid Spider Monkey-Particle Swarm Optimization for image dehazing.The large receptive field of atrous convolution extracts the global contextual information.The swarm based hybrid optimization is designed for tuning the neural network parameters during training.The experiments over the standard synthetic dataset images used in the proposed network recover clear output images free from distortion and halo effects.It is observed from the statistical analysis that Mean Square Error(MSE)decreases from 74.42 to 62.03 and Peak Signal to Noise Ratio(PSNR)increases from 22.53 to 28.82.The proposed method with hybrid optimization algorithm demonstrates a superior convergence rate and is a more robust than the current state-of-the-art techniques.

CLGA Net:Cross Layer Gated Attention Network for Image Dehazing

In this paper,we propose an end-to-end cross-layer gated attention network(CLGA-Net)to directly restore fog-free images.Compared with the previous dehazing network,the dehazing model presented in this paper uses the smooth cavity convolution and local residual module as the feature extractor,combined with the channel attention mechanism,to better extract the restored features.A large amount of experimental data proves that the defogging model proposed in this paper is superior to previous defogging technologies in terms of structure similarity index(SSIM),peak signal to noise ratio(PSNR)and subjective visual quality.In order to improve the efficiency of decoding and encoding,we also describe a fusion residualmodule and conduct ablation experiments,which prove that the fusion residual is suitable for the dehazing problem.Therefore,we use fusion residual as a fixed module for encoding and decoding.In addition,we found that the traditional defogging model based on the U-net network may cause some information losses in space.We have achieved effective maintenance of low-level feature information through the cross-layer gating structure that better takes into account global and subtle features.We also present the application of our CLGA-Net in challenging scenarios where the best results in both quantity and quality can be obtained.Experimental results indicate that the present cross-layer gating module can be widely used in the same type of network.

Recent Advances in Image Dehazing

Images captured in hazy or foggy weather conditions can be seriously degraded by scattering of atmospheric particles,which reduces the contrast,changes the color,and makes the object features difficult to identify by human vision and by some outdoor computer vision systems.Therefore image dehazing is an important issue and has been widely researched in the field of computer vision.The role of image dehazing is to remove the influence of weather factors in order to improve the visual effects of the image and provide benefit to post-processing.This paper reviews the main techniques of image dehazing that have been developed over the past decade.Firstly,we innovatively divide a number of approaches into three categories:image enhancement based methods,image fusion based methods and image restoration based methods.All methods are analyzed and corresponding sub-categories are introduced according to principles and characteristics.Various quality evaluation methods are then described,sorted and discussed in detail.Finally,research progress is summarized and future research directions are suggested.

Image Dehazing by Incorporating Markov Random Field with Dark Channel Prior

As one of the most simple and effective single image dehazing methods, the dark channel prior(DCP) algorithm has been widely applied. However, the algorithm does not work for pixels similar to airlight(e.g., snowy ground or a white wall), resulting in underestimation of the transmittance of some local scenes. To address that problem, we propose an image dehazing method by incorporating Markov random field(MRF) with the DCP. The DCP explicitly represents the input image observation in the MRF model obtained by the transmittance map. The key idea is that the sparsely distributed wrongly estimated transmittance can be corrected by properly characterizing the spatial dependencies between the neighboring pixels of the transmittances that are well estimated and those that are wrongly estimated. To that purpose, the energy function of the MRF model is designed. The estimation of the initial transmittance map is pixel-based using the DCP, and the segmentation on the transmittance map is employed to separate the foreground and background, thereby avoiding the block effect and artifacts at the depth discontinuity. Given the limited number of labels obtained by clustering, the smoothing term in the MRF model can properly smooth the transmittance map without an extra refinement filter. Experimental results obtained by using terrestrial and underwater images are given.

A Research on Single Image Dehazing Algorithms Based on Dark Channel Prior

In the field of computer and machine vision, haze and fog lead to image degradation through various degradation mechanisms including but not limited to contrast attenuation, blurring and pixel distortions. This limits the efficiency of machine vision systems such as video surveillance, target tracking and recognition. Various single image dark channel dehazing algorithms have aimed to tackle the problem of image hazing in a fast and efficient manner. Such algorithms rely upon the dark channel prior theory towards the estimation of the atmospheric light which offers itself as a crucial parameter towards dehazing. This paper studies the state-of-the-art in this area and puts forwards their strengths and weaknesses. Through experiments the efficiencies and shortcomings of these algorithms are shared. This information is essential for researchers and developers in providing a reference for the development of applications and future of the research field.

A single image dehazing method based on decomposition strategy

Outdoor haze has adverse impact on outdoor image quality,including contrast loss and poor visibility.In this paper,a novel dehazing algorithm based on the decomposition strategy is proposed.It combines the advantages of the two-dimensional variational mode decomposition(2DVMD)algorithm and dark channel prior.The original hazy image is adaptively decom-posed into low-frequency and high-frequency images according to the image frequency band by using the 2DVMD algorithm.The low-frequency image is dehazed by using the improved dark channel prior,and then fused with the high-frequency image.Furthermore,we optimize the atmospheric light and transmit-tance estimation method to obtain a defogging effect with richer details and stronger contrast.The proposed algorithm is com-pared with the existing advanced algorithms.Experiment results show that the proposed algorithm has better performance in comparison with the state-of-the-art algorithms.

Improved dark channel image dehazing method based on Gaussian mixture model

To solve the problem of color distortion after dehazing in the sky region by using the classical dark channel prior method to process the hazy images with large regions of sky,an improved dark channel image dehazing method based on Gaussian mixture model is proposed.Firstly,we use the Gaussian mixture model to model the hazy image,and then use the expectation maximization(EM)algorithm to optimize the parameters,so that the hazy image can be divided into the sky region and the non-sky region.Secondly,the sky region is divided into a light haze region,a medium haze region and a heavy haze region according to the different dark channel values to estimate the transmission respectively.Thirdly,the restored image is obtained by combining the atmospheric scattering model.Finally,adaptive local tone mapping for high dynamic range images is used to adjust the brightness of the restored image.The experimental results show that the proposed method can effectively eliminate the color distortion in the sky region,and the restored image is clearer and has better visual effect.

Single-Image Dehazing Based on Two-Stream Convolutional Neural Network

The haze weather environment leads to the deterioration of the visual effect of the image,and it is difficult to carry out the work of the advanced vision task.Therefore,dehazing the haze image is an important step before the execution of the advanced vision task.Traditional dehazing algorithms achieve image dehazing by improving image brightness and contrast or constructing artificial priors such as color attenuation priors and dark channel priors.However,the effect is unstable when dealing with complex scenes.In the method based on convolutional neural network,the image dehazing network of the encoding and decoding structure does not consider the difference before and after the dehazing image,and the image spatial information is lost in the encoding stage.In order to overcome these problems,this paper proposes a novel end-to-end two-stream convolutional neural network for single-image dehazing.The network model is composed of a spatial information feature stream and a highlevel semantic feature stream.The spatial information feature stream retains the detailed information of the dehazing image,and the high-level semantic feature stream extracts the multi-scale structural features of the dehazing image.A spatial information auxiliary module is designed and placed between the feature streams.This module uses the attention mechanism to construct a unified expression of different types of information and realizes the gradual restoration of the clear image with the semantic information auxiliary spatial information in the dehazing network.A parallel residual twicing module is proposed,which performs dehazing on the difference information of features at different stages to improve the model’s ability to discriminate haze images.The peak signal-to-noise ratio(PSNR)and structural similarity are used to quantitatively evaluate the similarity between the dehazing results of each algorithm and the original image.The structure similarity and PSNR of the method in this paper reached 0.852 and 17.557dB on the HazeRD dataset,which were higher than existing comparison algorithms.On the SOTS dataset,the indicators are 0.955 and 27.348dB,which are sub-optimal results.In experiments with real haze images,this method can also achieve excellent visual restoration effects.The experimental results show that the model proposed in this paper can restore desired visual effects without fog images,and it also has good generalization performance in real haze scenes.

Deeplearning method for single image dehazing based on HSI colour space

The traditional single image dehazing algorithm is susceptible to the prior knowledge of hazy image and colour distortion.A new method of deep learning multi-scale convolution neural network based on HSI colour space for single image dehazing is proposed in this paper,which directly learns the mapping relationship between hazy image and corresponding clear image in colour,saturation and brightness by the designed structure of deep learning network to achieve haze removal.Firstly,the hazy image is transformed from RGB colour space to HSI colour space.Secondly,an end-to-end multi-scale full convolution neural network model is designed.The multi-scale extraction is realized by three different dehazing sub-networks:hue H,saturation S and intensity I,and the mapping relationship between hazy image and clear image is obtained by deep learning.Finally,the model was trained and tested with hazy data set.The experimental results show that this method can achieve good dehazing effect for both synthetic hazy images and real hazy images,and is superior to other contrast algorithms in subjective and objective evaluations.

Image Dehazing Based on Haziness Analysis

We present two haze removal algorithms for single image based on haziness analysis.One algorithm regards haze as the veil layer,and the other takes haze as the transmission.The former uses the illumination component image obtained by retinex algorithm and the depth information of the original image to remove the veil layer.The latter employs guided filter to obtain the refined haze transmission and separates it from the original image.The main advantages of the proposed methods are that no user interaction is needed and the computing speed is relatively fast.A comparative study and quantitative evaluation with some main existing algorithms demonstrate that similar even better quality results can be obtained by the proposed methods.On the top of haze removal,several applications of the haze transmission including image refocusing,haze simulation,relighting and 2-dimensional(2D)to 3-dimensional(3D) stereoscopic conversion are also implemented.

HazeNet: a network for single image dehazing

In this letter, we present a novel integrated feature that incorporates traditional parameters, and adopt a parallel cascading fashion network Haze Net for enhancing image quality. Our unified feature is a complete integration, and its role is to directly describe the effects of haze. In Haze Net, we design two separate structures including backbone and auxiliary networks to extract feature map. Backbone network is responsible for extracting high-level feature map, and low-level feature learned by the auxiliary network can be interpreted as fine-grained feature. After cascading two features with different accuracy, final performance can be effectively improved. Extensive experimental results on both synthetic datasets and real-world images prove the superiority of the proposed method, and demonstrate more favorable performance compared with the existing state-of-art methods.

Nighttime image dehazing using color cast removal and dual path multi-scale fusion strategy

Nighttime image dehazing aims to remove the effect of haze on the images captured in nighttime,which however,raises new challenges such as severe color distortion,more complex lighting conditions,and lower contrast.Instead of estimating the transmission map and atmospheric light that are difficult to be accurately acquired in nighttime,we propose a nighttime image dehazing method composed of a color cast removal and a dual path multi-scale fusion algorithm.We first propose a human visual system(HVS)inspired color correction model,which is effective for removing the color deviation on nighttime hazy images.Then,we propose to use dual path strategy that includes an underexposure and a contrast enhancement path for multi-scale fusion,where the weight maps are achieved by selecting appropriate exposed areas under Gaussian pyramids.Extensive experiments demonstrate that the visual effect of the hazy nighttime images in real-world datasets can be significantly improved by our method regarding contrast,color fidelity,and visibility.In addition,our method outperforms the state-of-the-art methods qualitatively and quantitatively.

Image dehazing based on dark channel prior and brightness enhancement for agricultural monitoring

Obtaining clear and true images is a basic requirement for agricultural monitoring.However,under the influence of fog,haze and other adverse weather conditions,captured images are usually blurred and distorted,resulting in the difficulty of target extraction.Traditional image dehazing methods based on image enhancement technology can cause the loss of image information and image distortion.In order to address the above-mentioned problems caused by traditional image dehazing methods,an improved image dehazing method based on dark channel prior(DCP)was proposed.By enhancing the brightness of the hazed image and processing the sky area,the dim and un-natural problems caused by traditional image dehazing algorithms were resolved.Ten different test groups were selected from different weather conditions to verify the effectiveness of the proposed algorithm,and the algorithm was compared with the commonly-used histogram equalization algorithm and the DCP method.Three image evaluation indicators including mean square error(MSE),peak signal to noise ratio(PSNR),and entropy were used to evaluate the dehazing performance.Results showed that the PSNR and entropy with the proposed method increased by 21.81%and 5.71%,and MSE decreased by 40.07%compared with the original DCP method.It performed much better than the histogram equalization dehazing method with an increase of PSNR by 38.95%and entropy by 2.04%and a decrease of MSE by 84.78%.The results from this study can provide a reference for agricultural field monitoring.

CP-Net:Channel Attention and Pixel Attention Network for Single Image Dehazing

An end-to-end channel attention and pixel attention network(CP-Net)is proposed to produce dehazed image directly in the paper.The CP-Net structure contains three critical components.Firstly,the double attention(DA)module consisting of channel attention(CA)and pixel attention(PA).Different channel features contain different levels of important information,and CA can give more weight to relevant information,so the network can learn more useful information.Meanwhile,haze is unevenly distributed on different pixels,and PA is able to filter out haze with varying weights for different pixels.It sums the outputs of the two attention modules to improve further feature representation which contributes to better dehazing result.Secondly,local residual learning and DA module constitute another important component,namely basic block structure.Local residual learning can transfer the feature information in the shallow part of the network to the deep part of the network through multiple local residual connections and enhance the expressive ability of CP-Net.Thirdly,CP-Net mainly uses its core component,DA module,to automatically assign different weights to different features to achieve satisfactory dehazing effect.The experiment results on synthetic datasets and real hazy images indicate that many state-of-the-art single image dehazing methods have been surpassed by the CP-Net both quantitatively and qualitatively.

Dehazing for Image and Video Using Guided Filter

Poor visibility in bad weather, such as haze and fog, is a major problem for many applications of computer vision. Thus, haze removal is highly required for receiving high performance of the vision algorithm. In this paper, we propose a new fast dehazing method for real-time image and video processing. The transmission map estimated by an improved guided filtering scheme is smooth and respect with depth information of the underlying image. Results demonstrate that the proposed method achieves good dehazeing effect as well as real-time performance. The proposed algorithm, due to its speed and ability to improve visibility, may be used with advantages as pre-processing in many systems ranging from surveillance, intelligent vehicles, to remote sensing.

Single-image night haze removal based on color channel transfer and estimation of spatial variation in atmospheric light

The visible-light imaging system used in military equipment is often subjected to severe weather conditions, such as fog, haze, and smoke, under complex lighting conditions at night that significantly degrade the acquired images. Currently available image defogging methods are mostly suitable for environments with natural light in the daytime, but the clarity of images captured under complex lighting conditions and spatial changes in the presence of fog at night is not satisfactory. This study proposes an algorithm to remove night fog from single images based on an analysis of the statistical characteristics of images in scenes involving night fog. Color channel transfer is designed to compensate for the high attenuation channel of foggy images acquired at night. The distribution of transmittance is estimated by the deep convolutional network DehazeNet, and the spatial variation of atmospheric light is estimated in a point-by-point manner according to the maximum reflection prior to recover the clear image. The results of experiments show that the proposed method can compensate for the high attenuation channel of foggy images at night, remove the effect of glow from a multi-color and non-uniform ambient source of light, and improve the adaptability and visual effect of the removal of night fog from images compared with the conventional method.

一种基于暗亮通道分割融合的低照度环境图像去尘雾及增强方法

受煤矿井下粉尘、水雾和低照度环境影响,对皮带运输系统的监测图像精准识别极为困难。针对现有去尘雾方法的图像处理结果和效率欠佳的问题,提出一种基于暗亮通道分割融合的低照度环境图像去尘雾及增强方法。首先利用阈值分割结合伽马变换修正通道差,解决因低照度环境影响导致的尘雾浓度较大区域与其他区域间像素值差异不明显的问题,修正后通过引导尘雾图像做引导滤波得到更加符合实际情况的全局大气光强;然后为解决暗通道先验在尘雾浓度较大区域失效问题,引入亮通道先验进行补充,使用通道分量来辅助暗通道及亮通道透射率融合,避免因多次分割而导致的边缘像素归属问题;最后将去雾后RGB图像转至HSV空间,对亮度分量进行直方图均衡化并将均衡化前后的亮度分量进行加权融合,采用客观指标评价,选择最优聚合权值进行聚合,同时考虑去雾过程中饱和度损失和亮度分量与饱和度分量间的相关性提出饱和度自适应矫正函数,对图像饱和度进行矫正,色调分量保持不变,随后将图像转回至RGB空间,得到亮度适中、信息保留丰富和色彩鲜艳的图像;为验证所提方法的有效性,采用主观视觉、客观指标和目标检测精度及置信度进行算法对比,实验结果表明所提方法在上述4个指标上均优于被对比算法,其图像细节保留丰富,图像视觉观感更佳。

基于编解码多尺度特征优化的图像去雾算法

真实雾气不均匀分布的特点会导致基于合成数据集训练的网络对真实雾气下拍摄的图像的复原质量不佳.此外,现有去雾模型较大的网络参数量会影响去雾的实时性.针对这两个问题,提出一种参数量较低的基于编解码多尺度特征优化的图像去雾算法以去除真实场景下图像的雾气.首先,在编码部分利用跨通道上下文注意力隐式地建模像素间的关系,以恢复去雾后图像中物体的结构.然后,设计信息调节子网弥补编码器遗漏的浅层信息,解决细节恢复粗糙的问题.最后,在解码部分设计特征矫正子网,采用相减式残差结构减少噪声,保证输出结果的正确性.在多种真实雾数据集上,对所提方法的普适性进行实验.实验结果表明:在REVIDE真实雾数据集中,与MSBDN方法相比,所提方法在参数量降低46%的基础上获得了PSNR 1.25dB的提升;在OHaze、I-Haze以及RTTS多种室内外真实雾测试集中,与其他去雾方法相比,所提方法都取得了更好的PSNR结果和视觉效果.

基于颜色校正和深度信息去雾的水下感知系统

针对水下距离感知任务真实训练数据缺乏,水下目标感知任务目标模糊、密集、多尺度的问题,提出一种基于颜色校正和深度信息去雾的水下视觉感知系统。设计了一种改进的融合增强方法,并建立了一个水下单目图像数据集,以解决距离感知任务数据不足的难点。设计了一种基于深度信息的去雾方法,结合水下成像模型对图像进行去雾处理,提升图像质量。设计了一种基于中心点检测的通道重排网络,将卷积神经网络中浅层的详细特征完全集成到深层中,且无需锚框,增强对小目标、密集目标的特征提取能力。实验表明,该系统可从水下图像中恢复真实陆地色彩,准确感知水下场景相对距离,并实现域内和跨域高精度目标感知,在URPC数据集上取得了78.2%的域内目标检测精度,比基准CenterNet高出4.6%,在UTTS数据集上取得81.5%跨域目标检测精度,证明了该系统的有效性。