Cloud Image Prior: Single Image Cloud Removal

2021 ◽  
pp. 95-103
Author(s):  
Anirudh Maiya ◽  
S. S. Shylaja
2014 ◽  
Vol 281 ◽  
pp. 736-749 ◽  
Author(s):  
Ning He ◽  
Ke Lu ◽  
Bing-Kun Bao ◽  
Lu-Lu Zhang ◽  
Jin-Bao Wang

Author(s):  
Dingjian Jin ◽  
Mengqi Ji ◽  
Lan Xu ◽  
Gaochang Wu ◽  
Liejun Wang ◽  
...  

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 2014
Author(s):  
Sujy Han ◽  
Tae Bok Lee ◽  
Yong Seok Heo

Single image super-resolution task aims to reconstruct a high-resolution image from a low-resolution image. Recently, it has been shown that by using deep image prior (DIP), a single neural network is sufficient to capture low-level image statistics using only a single image without data-driven training such that it can be used for various image restoration problems. However, super-resolution tasks are difficult to perform with DIP when the target image is noisy. The super-resolved image becomes noisy because the reconstruction loss of DIP does not consider the noise in the target image. Furthermore, when the target image contains noise, the optimization process of DIP becomes unstable and sensitive to noise. In this paper, we propose a noise-robust and stable framework based on DIP. To this end, we propose a noise-estimation method using the generative adversarial network (GAN) and self-supervision loss (SSL). We show that a generator of DIP can learn the distribution of noise in the target image with the proposed framework. Moreover, we argue that the optimization process of DIP is stabilized when the proposed self-supervision loss is incorporated. The experiments show that the proposed method quantitatively and qualitatively outperforms existing single image super-resolution methods for noisy images.


Author(s):  
Zhenghao Shi ◽  
Meimei Zhu ◽  
Zheng Xia ◽  
Minghua Zhao

Images captured in hazy weather are usually of poor quality, which has a negative effect on the performance of outdoor computer imaging systems. Therefore, haze removal is critical for outdoor imaging applications. In this paper, a quick single-image dehazing method based on a new effective image prior, luminance dark prior, was proposed. This new image prior arose from the observation that most local patches in the luminance image of a haze-free outdoor YUV color space image usually contain pixels of very low intensity, which is similar to the dark channel prior used with HE for RGB images. Using this new prior, a transmission map was used to estimate the thickness of the haze in an image directly from the luminance component of the YUV color image. To obtain a transmission map with a clear edge outline and depth layer of scene objects, a joint filter containing a bilateral filter and Laplacian operator was employed. Experimental results demonstrated that the proposed method unveiled details and recovered vivid colors even in heavily hazy regions, and provided superior visual effects to many other existing methods.


Optik ◽  
2013 ◽  
Vol 124 (20) ◽  
pp. 4429-4434 ◽  
Author(s):  
Ming-zhu Shi ◽  
Ting-fa Xu ◽  
Liang Feng ◽  
Jiong Liang ◽  
Kun Zhang

2013 ◽  
Vol 850-851 ◽  
pp. 825-829
Author(s):  
Guo Dong Jin ◽  
Li Bin Lu ◽  
Xiao Fei Zhu

Using dark channel prior to estimate the thickness of the haze , recent research work has made significant progresses in single image dehazing. However , it is difficult to apply existing method for processing high resolution input images because of t he heavy computation cost s of it . For some kinds of input images , existing method still can not reach enough accuracy . we develop a powerful and practical single image dehazing method. The experimental results show our gradient prior of transmission map s greatly reduces t he computation cost s of t he previous method. Furthermore , the optimization methods and parameter adjustment for our novel image prior enhance t he accuracy of the computation related with transmission map. Overall , compared wit h the state of the art , our new single image dehazing method achieves t he same, and even better image quality with only around 1/8 computation time and memory cost .


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