Multi-Scale Deep Residual Learning-Based Single Image Haze Removal via Image Decomposition

Yeh, Chia‐Hung; Huang, Chih-Hsiang; Li, Kang

doi:10.1109/tip.2019.2957929

Cited by 120 publications

(48 citation statements)

References 50 publications

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“…In this network, a novel ranking layer was proposed to extend the structure of CNN such that the statistical and structural attributes of hazy images could be simultaneously captured. In 2019, Yeh et al [27] proposed a deep learning-based architecture (denoted by MSRL-DehazeNet) for single-image haze removal relying on multiscale residual learning (MSRL) and image decomposition. They reformulated the dehazing problem as restoration of the image base component.…”

Section: A Image Defoggingmentioning

confidence: 99%

RASWNet: An Algorithm That Can Remove All Severe Weather Features from a Degraded Image

Gao

Long

et al. 2020

IEEE Access

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The advanced driving assistant system (ADAS) is an important vehicle safety technology that can effectively reduce traffic accidents. This system can perceive information about the surrounding environment through in-vehicle cameras. However, these cameras are easily affected by severe weather conditions, such as those involving fog, rain, and snow. The quality of the images acquired by the system is degraded, and the function of the ADAS is thus weakened. In response to this problem, we propose a comprehensive imaging model that can represent the features of fog, rain streaks, raindrops and snowflakes in an image. Subsequently, an algorithm called RASWNet is proposed, which can remove all severe weather features from a degraded image. Based on the generative adversarial network, RASWNet combines the focus capture ability of a visual attention mechanism, the memory ability of the recurrent neural network and the feature extraction ability of the dense blocks approach. We verify the network structure through several ablation studies and use various synthetic and real images to test it. The results of these experiments show that our algorithm is not only better than the commonly used algorithms in terms of its clarity enhancement capacity but is also suitable for all severe weather conditions. INDEX TERMS Generative adversarial network, remove all severe weather features, degraded image, RASWNet, visual attention mechanism.

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Section: A Image Defoggingmentioning

confidence: 99%

RASWNet: An Algorithm That Can Remove All Severe Weather Features from a Degraded Image

Gao

Long

et al. 2020

IEEE Access

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“…Figure 19 shows experiment results of the single image haze removal algorithm based on the transposed filter. The visual quality is equivalent to the current state-of-the-art dehaze algorithms [10][11][12][13][14][29][30][31][32][33][34], and even some details are brighter in color.…”

Section: Experiments and Results Evaluationmentioning

confidence: 99%

“…The haze removal algorithm is also drawing much attention in remote sensing areas recently [29][30][31]. Deep learningbased dehazing algorithms have achieved good dehazing performance [18][19][20][21][22][23][24][25][26][32][33][34], but their disadvantage is also obvious. The quality of these networks depends on the dataset.…”

Section: Introductionmentioning

confidence: 99%

Accelerating Haze Removal Algorithm Using CUDA

Wang

et al. 2020

Remote Sensing

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The dark channel prior (DCP)-based single image removal algorithm achieved excellent performance. However, due to the high complexity of the algorithm, it is difficult to satisfy the demands of real-time processing. In this article, we present a Graphics Processing Unit (GPU) accelerated parallel computing method for the real-time processing of high-definition video haze removal. First, based on the memory access pattern, we propose a simple but effective filter method called transposed filter combined with the fast local minimum filter algorithm and integral image algorithm. The proposed method successfully accelerates the parallel minimum filter algorithm and the parallel mean filter algorithm. Meanwhile, we adopt the inter-frame atmospheric light constraint to suppress the flicker noise in the video haze removal and simplify the estimation of atmospheric light. Experimental results show that our implementation can process the 1080p video sequence with 167 frames per second. Compared with single thread Central Processing Units (CPU) implementation, the speedup is up to 226× with asynchronous stream processing and qualified for the real-time high definition video haze removal.

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“…Accordingly, Ren et al [90] addressed their own limitations by adopting an additional CNN known as the holistic edge guided network to enforce the transmittance smoothness inside the same object. Yeh et al [91] exploited image decomposition to visibility restoration by dehazing the base layer and enhancing the detail layer. The dehazing task leveraged the multiscale network that was developed by Ren et al [89] for structural feature extraction and the encoder-decoder framework for statistical feature extraction.…”

Section: Deep Learningmentioning

confidence: 99%

Visibility Restoration: A Systematic Review and Meta-Analysis

Ngo

Lee

Ngo

et al. 2021

Sensors

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Image acquisition is a complex process that is affected by a wide variety of internal and environmental factors. Hence, visibility restoration is crucial for many high-level applications in photography and computer vision. This paper provides a systematic review and meta-analysis of visibility restoration algorithms with a focus on those that are pertinent to poor weather conditions. This paper starts with an introduction to optical image formation and then provides a comprehensive description of existing algorithms as well as a comparative evaluation. Subsequently, there is a thorough discussion on current difficulties that are worthy of a scientific effort. Moreover, this paper proposes a general framework for visibility restoration in hazy weather conditions while using haze-relevant features and maximum likelihood estimates. Finally, a discussion on the findings and future developments concludes this paper.

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Multi-Scale Deep Residual Learning-Based Single Image Haze Removal via Image Decomposition

Cited by 120 publications

References 50 publications

RASWNet: An Algorithm That Can Remove All Severe Weather Features from a Degraded Image

RASWNet: An Algorithm That Can Remove All Severe Weather Features from a Degraded Image

Accelerating Haze Removal Algorithm Using CUDA

Visibility Restoration: A Systematic Review and Meta-Analysis

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