Deep Residual Haze Network for Image Dehazing and Deraining

Wang, Chuansheng; Li, Zuoyong; Wu, Jiawei; Fan, Haoyi; Xiao, Guobao; Zhang, Hong

doi:10.1109/access.2020.2964271

Cited by 41 publications

(11 citation statements)

References 102 publications

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“… The learning-based algorithms can directly output a clear images without using ASM. Such a strategy can achieve good dehazing performance on some datasets [ 36 , 37 , 38 ]. As CNN can have multiple outputs, one of the branches can directly output haze-free images.…”

Section: Related Workmentioning

confidence: 99%

Color-Dense Illumination Adjustment Network for Removing Haze and Smoke from Fire Scenario Images

Wang

Luo

et al. 2022

Sensors

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The atmospheric particles and aerosols from burning usually cause visual artifacts in single images captured from fire scenarios. Most existing haze removal methods exploit the atmospheric scattering model (ASM) for visual enhancement, which inevitably leads to inaccurate estimation of the atmosphere light and transmission matrix of the smoky and hazy inputs. To solve these problems, we present a novel color-dense illumination adjustment network (CIANet) for joint recovery of transmission matrix, illumination intensity, and the dominant color of aerosols from a single image. Meanwhile, to improve the visual effects of the recovered images, the proposed CIANet jointly optimizes the transmission map, atmospheric optical value, the color of aerosol, and a preliminary recovered scene. Furthermore, we designed a reformulated ASM, called the aerosol scattering model (ESM), to smooth out the enhancement results while keeping the visual effects and the semantic information of different objects. Experimental results on both the proposed RFSIE and NTIRE’20 demonstrate our superior performance favorably against state-of-the-art dehazing methods regarding PSNR, SSIM and subjective visual quality. Furthermore, when concatenating CIANet with Faster R-CNN, we witness an improvement of the objection performance with a large margin.

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Section: Related Workmentioning

confidence: 99%

Color-Dense Illumination Adjustment Network for Removing Haze and Smoke from Fire Scenario Images

Wang

Luo

et al. 2022

Sensors

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“…However, these algorithms still need paired data for training. It is very difficult to many tasks to collect images, such as image dehazing and image denoising [31]- [36]. CycleGAN [37], Dual-GAN [38], DiscoGAN [39] and Coupled GAN [40] were proposed to solve this problem.…”

Section: B Image-to-image Style Transfermentioning

confidence: 99%

MSGAN: Generative Adversarial Networks for Image Seasonal Style Transfer

Zhang

Wang

2020

IEEE Access

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Although Generative Adversarial Networks (GANs) have shown remarkable successes in various computer vision tasks, they still face challenges in image season style transfer task. In this paper, we propose a multi-season Generative Adversarial Networks (MSGANs) aimed to transfer input images into other season styles. To improve the quality of the simulated images generated by the proposed MSGAN, we propose a novel loss function to guide the optimization direction of the network. Besides, we adopt the saliency information to guide the seasonal style transformation task, so as to ensure that different image contents can have different optimization weights in MSGAN. The experimental results show that the proposed MSGAN can generate high-quality simulated images from real images, and is superior to other latest methods. Not only that, the synthetic image generated by the proposed method also be used to perform depth estimation task so that prove that the synthetic images can be well applied to other computer vision tasks.

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“…Recently, there have been an increasing number of learning-based methods that can achieve accurate and fast haze removal, such as employing random forests [23], color attenuation [24], [25], and deep learning [26]- [38]. DehazeNet [39] developed a convolutional neural network to estimate the transmission map of hazy images and then restore haze-free images by estimating atmospheric light.…”

Section: Introductionmentioning

confidence: 99%

MCPA: A Fast Single Image Haze Removal Method Based on the Minimum Channel and Patchless Approach

Fuh

Tung

2022

IEEE Access

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The image haze removal algorithm is challenging regarding computational processing speed and the hazy removal effect. Instead of using the local patch approach, which assumes the scene transmission to be locally constant and uses various filters to smooth the transmission map, this paper proposes a fast single image haze removal method based on a minimum channel and patchless approach. A new simple approach to estimate the atmospheric light and the scene transmission is proposed based on the minimum channel of images. The histogram of the minimum channel of the image is used to extract the atmospheric light pixels and exclude the non-hazy bright pixels in the image. The histogram equalization and image multiplication are applied to achieve better visual quality. In order to verify the performance of the proposed method, 100 images are collected from datasets I-HAZE, O-HAZE, and websites. Experimental results show that our proposed method outperforms up-to-date state-of-the-art haze removal algorithms using quantitative evaluations. From subjective comparisons, the proposed method outperforms most current haze removal algorithms in color restoration. Also, time assessment results show that our proposed method is the fastest among the up-to-date state-of-the-art haze removal methods and is about 15 times faster than the secondfastest method. The main contribution of the proposed method is significantly reducing computation time because it uses a patchless approach that does not need any filter and complicated algorithms. In addition to significantly reducing the computational processing speed, our proposed method can achieve better visual quality.

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Deep Residual Haze Network for Image Dehazing and Deraining

Cited by 41 publications

References 102 publications

Color-Dense Illumination Adjustment Network for Removing Haze and Smoke from Fire Scenario Images

Color-Dense Illumination Adjustment Network for Removing Haze and Smoke from Fire Scenario Images

MSGAN: Generative Adversarial Networks for Image Seasonal Style Transfer

MCPA: A Fast Single Image Haze Removal Method Based on the Minimum Channel and Patchless Approach

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