An Efficient Rain Streaks Detection and Removal for Single Image Using Hybridization of Rain Detection and Rain Removal Technique (HyDRA)

Hassim, Raima; Sulaiman, Hamzah Asyrani; Abdullah, Baharudin

doi:10.1166/asl.2018.10680

Cited by 2 publications

(3 citation statements)

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“…In 2014, Pei et al [6] proposed to detect rain and snow in images based on saturation and visibility. In 2018, Hassim et al [7] proposed a hybridization of rain detection and removal technique based on dictionary learning. In 2020, Wang et al [8] first detected the rain locations with some specially designed filters and proposed an effective linear model for rain removal.…”

Section: Two-stage Approachesmentioning

confidence: 99%

“…In 2018, Hassim et al. [7] proposed a hybridization of rain detection and removal technique based on dictionary learning. In 2020, Wang et al.…”

Section: Related Workmentioning

confidence: 99%

“…A straightforward method to remove rain from images/ videos is to adopt a two-stage strategy of detection and repair [2][3][4][5][6][7][8]. However, these methods tend to falsely detect non-rain image boundaries that are consistent with the orientation of the rain, and sometimes blur or even damage the non-rain parts.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A new two‐stage method for single image rain removal

Zhu

Liu

Luo

et al. 2020

IET Image Processing

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Compared with video de‐raining, single image de‐raining is more technically difficult due to the lack of temporally redundant information. This paper proposes a new two‐stage method for single image de‐raining. In the first stage, the authors develop an effective two‐step model to detect the rain streaks by taking pixel intensity, and direction of rain streaks as priors. In the second stage, the rain repair process is performed at the patch level. The authors first define a way to search for similar patches of each patch, and then group the similar patches together to form a matrix. Finally, a low‐rank matrix completion technique is utilized to recover the rain‐stained pixels based on the rain map obtained from the first stage. Compared with several state‐of‐the‐art methods, authors' proposed method is competitive in terms of the abilities of removing rain streaks, and preserving image details.

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Section: Two-stage Approachesmentioning

confidence: 99%

“…In 2018, Hassim et al. [7] proposed a hybridization of rain detection and removal technique based on dictionary learning. In 2020, Wang et al.…”

Section: Related Workmentioning

confidence: 99%