Cycle-Dehaze: Enhanced CycleGAN for Single Image Dehazing

By Deniz Engin, Anil Genç, Hazim Kemal Ekenel. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018

589 citations recorded by the source corpus.

In this paper, we present an end-to-end network, called Cycle-Dehaze, for single image dehazing problem, which does not require pairs of hazy and corresponding ground truth images for training. That is, we train the network by feeding clean and hazy images in an unpaired manner. Moreover, the proposed approach does not rely on estimation of the atmospheric scattering model parameters. Our method enhances CycleGAN formulation by combining cycle-consistency and perceptual losses in order to improve the quality of textural information recovery and generate visually better haze-free images. Typically, deep learning models for dehazing take low resolution images as input and produce low resolution outputs. However, in the NTIRE 2018 challenge on single image dehazing, high resolution images were provided. Therefore, we apply bicubic downscaling. After obtaining low-resolution outputs from the network, we utilize the Laplacian pyramid to upscale the output images to the original resolution. We conduct experiments on NYU-Depth, I-HAZE, and O-HAZE datasets. Extensive experiments demonstrate that the proposed approach improves CycleGAN method both quantitatively and qualitatively.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/dc43cf3ac585a832a6ec523ab54b201cd30408e5

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Citations in the corpus

A Survey on Generative Adversarial Networks: Variants, Applications, and Training (2020, methodology): “t for refining the transformed face. 3.1.21 Others image domain applications There is the numbers of GAN models that have also been used in many other image domains applications, such as Cycle-Dehaze [127], Disentangled-Dehazing [128] and Cycle-Defog2Refog [129] for image de-hazing; Alpha-GAN [130] ”
Artificial intelligence in the creative industries: a review (2020, methodology): “2) in order to improve the quality of textural information recovery and generate visually better haze-free images [148].”
Image-to-Image Translation: Methods and Applications (2021, background): “Image deraining [195], [196], [197], image dehazing [199], [200], [201], [198], [202] and image deblurring [206], [203], [204], [205] aim to remove optical distortions from photos that were taken out of focus or while the camera was moving, or from photos of faraway geographical or astronomical feat”
A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications (2020, methodology): “GANs have been utilized in other image processing and computer vision tasks [354]–[357] such as object transfiguration [358], [359], semantic segmentation [360], visual saliency prediction [361], object tracking [362], [363], image dehazing [364]–[366], natural image matting [367], image inpainting ”
ALL Snow Removed: Single Image Desnowing Algorithm Using Hierarchical Dual-tree Complex Wavelet Representation and Contradict Channel Loss (2021, background): “Zheng [5] Eigen [35] DesnowNet [1] CycleGAN [36] DAD [37] All in One [38] JSTASR [2] Ours”