Despite the recent progress in image dehazing, several problems remain largely unsolved such as robustness for varying scenes, the visual quality of reconstructed images, and effectiveness and flexibility for applications. To tackle these problems, we propose a new deep network architecture for single image dehazing called DR-Net. Our model consists of three main subnetworks: a transmission prediction network that predicts transmission map for the input image, a haze removal network that reconstructs latent image steered by the transmission map, and a refinement network that enhances the details and color properties of the dehazed result via weakly supervised learning. Compared to previous methods, our method advances in three aspects: (i) pure data-driven model; (ii) the end-to-end system; (iii) superior robustness, accuracy, and applicability. Extensive experiments demonstrate that our DR-Net outperforms the state-of-the-art methods on both synthetic and real images in qualitative and quantitative metrics. Additionally, the utility of DR-Net has been illustrated by its potential usage in several important computer vision tasks.
Supplementary information based methods usually require additional knowledge such as 3D geographical models @cite_39 , scene depth @cite_28 , multiple images of the scene under different weather conditions @cite_6 , polarization filters @cite_34 , and so forth. Nevertheless, these methods are mostly computationally intensive and not applicable to dynamic scenes. Much attention, therefore, has been devoted to single image dehazing methods @cite_18 @cite_0 @cite_23 @cite_29 @cite_5 @cite_1 @cite_40 @cite_2 @cite_11 @cite_16 @cite_32 . In @cite_23 , He al observed an interesting phenomenon of haze-free outdoors images that at least one channel has some pixels with very low intensities. Based upon this prior, the transmission and global atmospheric light were roughly estimated. After that, the dehazed image was achieved based on the refined transmission by soft-matting @cite_19 or guided filter @cite_30 as well as the estimated global atmospheric light. In @cite_17 , Berman al proposed a nonlocal image dehazing method, which relied on the assumption that colors of a haze-free image are well approximated by a few hundred distinct colors that form tight clusters in RGB space.
@cite_30 @cite_18 @cite_28 @cite_29 @cite_1 @cite_32 @cite_6 @cite_39 @cite_0 @cite_16 @cite_40 @cite_19 @cite_17 @cite_23 @cite_2 @cite_5 @cite_34 @cite_11
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Most existing image dehazing methods tend to boost local image contrast for regions with heavy haze. Without special treatment, these methods may significantly amplify existing image artifacts such as noise, color aliasing and blocking, which are mostly invisible in the input images but are visually intruding in the results. This is especially the case for low quality cellphone shots or compressed video frames. The recent work of (2014) addresses blocking artifacts for dehazing, but is insufficient to handle other artifacts. In this paper, we propose a new method for reliable suppression of different types of visual artifacts in image and video dehazing. Our method makes contributions in both the haze estimation step and the image recovery step. Firstly, an image-guided, depth-edge-aware smoothing algorithm is proposed to refine the initial atmosphere transmission map generated by local priors. In the image recovery process, we propose Gradient Residual Minimization (GRM) for jointly recovering the haze-free image while explicitly minimizing possible visual artifacts in it. Our evaluation suggests that the proposed method can generate results with much less visual artifacts than previous approaches for lower quality inputs such as compressed video clips. 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. Photographs of hazy scenes typically suffer having low contrast and offer a limited visibility of the scene. This article describes a new method for single-image dehazing that relies on a generic regularity in natural images where pixels of small image patches typically exhibit a 1D distribution in RGB color space, known as color-lines. We derive a local formation model that explains the color-lines in the context of hazy scenes and use it for recovering the scene transmission based on the lines' offset from the origin. The lack of a dominant color-line inside a patch or its lack of consistency with the formation model allows us to identify and avoid false predictions. Thus, unlike existing approaches that follow their assumptions across the entire image, our algorithm validates its hypotheses and obtains more reliable estimates where possible. In addition, we describe a Markov random field model dedicated to producing complete and regularized transmission maps given noisy and scattered estimates. Unlike traditional field models that consist of local coupling, the new model is augmented with long-range connections between pixels of similar attributes. These connections allow our algorithm to properly resolve the transmission in isolated regions where nearby pixels do not offer relevant information. An extensive evaluation of our method over different types of images and its comparison to state-of-the-art methods over established benchmark images show a consistent improvement in the accuracy of the estimated scene transmission and recovered haze-free radiances. We present an approach for easily removing the effects of haze from passively acquired images. Our approach is based on the fact that usually the natural illuminating light scattered by atmospheric particles (airlight) is partially polarized. Optical filtering alone cannot remove the haze effects, except in restricted situations. Our method, however, stems from physics-based analysis that works under a wide range of atmospheric and viewing conditions, even if the polarization is low. The approach does not rely on specific scattering models such as Rayleigh scattering and does not rely on the knowledge of illumination directions. It can be used with as few as two images taken through a polarizer at different orientations. As a byproduct, the method yields a range map of the scene, which enables scene rendering as if imaged from different viewpoints. It also yields information about the atmospheric particles. We present experimental results of complete dehazing of outdoor scenes, in far-from-ideal conditions for polarization filtering. We obtain a great improvement of scene contrast and correction of color.