Single-image deraining using an adaptive nonlocal means filter

Kim, Jin Hwan; Lee, Chul; Sim, Jae-Young; Kim, Chang-Su

doi:10.1109/icip.2013.6738189

Cited by 237 publications

(144 citation statements)

References 9 publications

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“…Kim et al proposed a single-image de-raining algorithm using an adaptive spatial NLM filter [7]. They first detect rain streak regions based on a kernel regression method.…”

Section: Related Workmentioning

confidence: 99%

“…Rain detection algorithms are usually classified into three categories: optical approaches, spatial-domain approaches, and frequency-domain approaches [1][2][3][4][5][6][7][8].…”

Section: Related Workmentioning

confidence: 99%

“…Most of the conventional rain removal schemes assume a static camera environment or use only a single frame [1,2,6,7]. Such an assumption simplifies a de-raining problem, but often becomes a major cause of visual-quality degradation when a camera shooting environment violates the assumption.…”

Section: Introductionmentioning

confidence: 99%

“…Many approaches for detecting and removing rain streaks in images and restoring the original scenes have been developed [1][2][3][4][5][6][7][8]. Most of the conventional rain removal schemes assume a static camera environment or use only a single frame [1,2,6,7].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Multi-frame de-raining algorithm using a motion-compensated non-local mean filter for rainy video sequences

Kim

Seo

Song

2015

Journal of Visual Communication and Image Representation

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“…Kim et al proposed a single-image de-raining algorithm using an adaptive spatial NLM filter [7]. They first detect rain streak regions based on a kernel regression method.…”

Section: Related Workmentioning

confidence: 99%

“…Rain detection algorithms are usually classified into three categories: optical approaches, spatial-domain approaches, and frequency-domain approaches [1][2][3][4][5][6][7][8].…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multi-frame de-raining algorithm using a motion-compensated non-local mean filter for rainy video sequences

Kim

Seo

Song

2015

Journal of Visual Communication and Image Representation

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“…The minimization technique can globally control how many non-zero gradients are resulted in the image. In [7], Kim et al proposed a two-stage method for rain removal. In the first stage, rain streaks are detected by using a kernel regression method under an assumption of the elliptical shape and the vertical orientation of rain steaks.…”

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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Single image deraining using multi‐stage and multi‐scale joint channel coordinate attention fusion network

Yang

Zhang

Cui

et al. 2022

Int J of Intelligent Sys

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Rain streaks can seriously degrade the visual quality of an image and are detrimental to subsequent algorithms such as object detection and semantic segmentation. Therefore, removing rain streaks is a very important task. The deraining task has two main limitations: the first is to encode information about rain streaks in different densities and directions, the second is to keep the background details of the image while removing the rain streak. To address these limitations, we propose an effective algorithm, called multi-stage and multi-scale joint channel coordinate attention fusion network (MMAFN). We mainly propose a two-stage network structure, both of which use an encoderdecoder network to extract features. The first-stage network extracts coarse features and the second-stage network integrates the features of the former to further refine features. We design the joint channel coordinate attention block to encode features of rain streaks in different directions and densities. In addition, to better fuse features of different scales and enhance the generalization performance of the network, the inception attention branch block and the multi-level feature fusion block are designed. Extensive experiments substantiate the superiority of the proposed network

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Single-image deraining using an adaptive nonlocal means filter

Cited by 237 publications

References 9 publications

Multi-frame de-raining algorithm using a motion-compensated non-local mean filter for rainy video sequences

Multi-frame de-raining algorithm using a motion-compensated non-local mean filter for rainy video sequences

Weighted median guided filtering method for single image rain removal

Single image deraining using multi‐stage and multi‐scale joint channel coordinate attention fusion network

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