RYF-Net: Deep Fusion Network for Single Image Haze Removal

Dudhane, Akshay; Murala, Subrahmanyam

doi:10.1109/tip.2019.2934360

Cited by 68 publications

(19 citation statements)

References 39 publications

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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%

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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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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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“…According to [32], when |I − A| ≤ K , the region is a bright area, and J(x ) = I(x ). When |I − A| ≥ K , the region satisfies the dark channel prior, the original transmission map remains unchanged, and the image is restored with (16), where K is the defined threshold. In contrast to [32], when |I − A| ≤ K , the transmission is recalculated so that the t (x ) in the sky is more consistent with t real (x ) and unified with the atmospheric scattering model.…”

Section: Restoration Model After Improvement Of the Sky Area Transmismentioning

confidence: 99%

Sand-Dust Image Restoration Based on Reversing the Blue Channel Prior

et al. 2020

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Images acquired under sand-dust weather conditions are severely degraded, with low contrast and severe color shift. The reason is that, due to the influence of sand-dust particles, light is scattered and absorbed, resulting in a blurred image and low contrast; the color shift is caused by the rapid attenuation of blue light. Therefore, to solve the problem of color shift and poor visibility in sand-dust images, this paper proposes a sand-dust image restoration method based on reversing the blue channel prior (RBCP). Under the influence of the blue channel, the dark channel prior (DCP) method will fail. Therefore, the method first reverses the blue channel of the sand-dust image and uses the dark channel prior method, which we call RBCP, and then, RBCP is used to estimate the atmospheric light and transmission map and recover the sand-dust image. The restored image shows significantly improved visibility. When estimating the transmission map, a guiding filter is used to improve the coarse transmission map, and a tolerance mechanism is introduced to modify the transmission map of bright areas in the sky to solve the problem of distortion in the sky. Finally, combined with the gray world, an adaptive color adjustment factor is introduced into the restoration model to remove the color shift. Experimental results via qualitative and quantitative evaluation demonstrate that the proposed method can effectively recover clear sand-dust images and produce results superior to those of other state-of-the-art methods.

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“…Subsequently, they devised a DehazeNet-like CNN for dehazing only the Y channel; hence, this network was lightweight while retaining comparable performance. Dudhane and Murala [87] furthered the previous work by utilizing two DehazeNet-like CNNs for estimating two versions of the medium transmittance in RGB and YCbCr color spaces. Therefore, they fused the two transmittance estimates using a fusion network to obtain a final transmittance.…”

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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RYF-Net: Deep Fusion Network for Single Image Haze Removal

Cited by 68 publications

References 39 publications

Accelerating Haze Removal Algorithm Using CUDA

Accelerating Haze Removal Algorithm Using CUDA

Sand-Dust Image Restoration Based on Reversing the Blue Channel Prior

Visibility Restoration: A Systematic Review and Meta-Analysis

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