Single-Image-Based Rain and Snow Removal Using Multi-guided Filter

Zheng, Xianhui; Liao, Yinghao; Guo, Weinong; Fu, Xueyang; Ding, Xinghao

doi:10.1007/978-3-642-42051-1_33

Cited by 110 publications

(88 citation statements)

References 10 publications

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“…In Figure 7 we compare our rain removal result with the methods of Xu et al [14] and the coauthors's previous work in [16]. It shows that the proposed method yields better visual quality against other methods for comparisons.…”

Section: Comparisonsmentioning

confidence: 62%

“…Fig. 7 Comparison of rain removal results with other methods Figure 8 shows the comparisons of snow removal results obtained by previous methods [14,16] with the proposed method. Clearly, the proposed method achieves much more accurate result of snowflake removal than the previous methods.…”

Section: Comparisonsmentioning

confidence: 99%

“…Xu et al proposed a method using guided filter to remove rain or snow [14]. Zheng et al proposed a new method to remove rain or snow through using guided filter to get the smooth image [16]. Yi-Lei Chen and Chiou-Ting Hsu proposed a novel low-rank appearance model for removing rain streaks [5].…”

Section: Introductionmentioning

confidence: 99%

“…Even these denoising methods can directly apply for single image rain or snow removal, the effects are poor and the results are always artificially blurred. Whatsoever, for many single image rain removal methods, these denoising methods can be a good pre-processing, such as [6,12,14,16] and the proposed method.…”

Section: Introductionmentioning

confidence: 99%

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Single image rain and snow removal via guided L0 smoothing filter

Ding

Chen

Zheng

et al. 2015

Multimed Tools Appl

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Since no temporal information can be exploited, rain and snow removal from single image is a challenging problem. In this paper, an improved rain and snow removal method from single image is proposed by designing a guided L0 smoothing filter. The designed filter is inspired by the previous L0 gradient minimization. Then a coarse rain-free or snow-free image can be obtained with the proposed filter, and the final refined result is recovered by a further minimization operation depending on the observed image. Experimental results show that the proposed algorithm generates better or comparable outputs than the state-of-the-art algorithms in rain and snow removal task for single image.

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Section: Comparisonsmentioning

confidence: 62%

Section: Comparisonsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Single image rain and snow removal via guided L0 smoothing filter

Ding

Chen

Zheng

et al. 2015

Multimed Tools Appl

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“…Though the method is effective for images with a simple structure, some desirable details in images with complex structures are usually removed. In [8], Zheng et al separated the low-frequency part of input image using guided filter, and experiments show that the results are better than those of using bilateral filter. Apart from the abovementioned filters, the weighted median filter [9] is also a better alternation for the median filter to effectively filter images while not strongly blurring edges.…”

Section: Introductionmentioning

confidence: 99%

Weighted median guided filtering method for single image rain removal

Shi

Zhang³

et al. 2018

J Image Video Proc.

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Because there is no temporal information available, rain removal with a single image is more challenging than that with a video. In this paper, we present a weighted median guided filtering method for rain removal with a single image. It consists of two filtering operations. Firstly, a weighted median filter is convoluted with an input rainy image to obtain a coarse rain-free image; then, guided filter is employed to obtain a refined rain-free image, where the coarse rain-free image is used as a guided image and convoluted with the input rainy image via guided filter. Experimental results show that the proposed method generated comparable results to the state-of-the-art algorithms with low computation cost.

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