Rain Streaks Removal in Image via Decomposition and Visibility Feature Saturation

Katre, Ruchi; Dodkey, Nitesh

doi:10.24999/ijoaem/02040021

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…; ; (7) Where the number of true shadow pixels are denoted false negative (FN) which are identified as non-shadow pixels. The number of non-shadow pixels, identified as shadow pixels are denoted by False positive (FP).…”

Section: A Anisotropic Local Colour Constancymentioning

confidence: 99%

Comparative Analysis of Single Image Shadow Detection and Removal in Aerial Images

Mishra¹,

Chaurasia²,

Kurmi

2017

ijoaem

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Section: A Anisotropic Local Colour Constancymentioning

confidence: 99%

Comparative Analysis of Single Image Shadow Detection and Removal in Aerial Images

Mishra¹,

Chaurasia²,

Kurmi

2017

ijoaem

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“…Our vision system is very sensitive regarding shadows and us also able to track the objects along with shadows [2]. Shadows can be aided for scene interpretation, base on model shadows which may be include or ignore [3]. In some case, shadows degrade the quality of image [3].…”

Section: Introductionmentioning

confidence: 99%

“…Shadows can be aided for scene interpretation, base on model shadows which may be include or ignore [3]. In some case, shadows degrade the quality of image [3]. Shadow detection is an essential pre-processing action for image enhancement [4].…”

Section: Introductionmentioning

confidence: 99%

Modified Hue over Intensity Ratio Based Method for Shadow Detection and Removal in Arial Images

Mishra¹,

Chaurasia²

2017

ijoaem

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Single image deraining via wide rectangular regional blocks and dual attention complementary enhancement network

Shen,

Zhang,

et al. 2024

Sci Rep

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Rain is a common weather phenomenon, and the challenge of removing rain streaks from a single image is crucial due to its detrimental impact on image quality and the extraction of valuable background information. Existing methods commonly rely on specific assumptions regarding rain models, which restricts their ability to accommodate a wide range of real-world scenarios. To overcome this limitation, these methods often require complex optimization techniques or stepwise refinement strategies. In this paper, we propose a novel wide rectangular regional block and dual attention complementary enhancement deraining kernel prediction subnet to meet the challenge. The network called WRRDANet consists of a kernel prediction subnet and pixel-wise dilation filtering. In the kernel prediction subnet, we capture more specific contextual background information and complex pixel-wise kernels. Afterward, the learned pixel-wise multi-scale kernels from the kernel prediction subnet are used to perform dilation filtering on the original rainy image, effectively restoring richer background details by expanding the scope of deraining to a larger extent. We conducted a comprehensive evaluation using synthetic and real rainfall datasets to demonstrate the effectiveness of our approach. The results, both qualitatively and quantitatively, indicate that our approach outperforms other popular rain removal methods.Rain streaks cause significant degradation in the quality of rainy images or video content, posing challenges for various outdoor computer vision systems, including target detection and recognition 1 , drone piloting 2 , surveillance systems 3 , and scene analysis 4 . Depending on the kind of input data, the existing algorithms may be divided into two categories: single image-based 5-7 and video-based 8-11 rain removal methods. Since a video is composed of frames of images, developing specialized image-deraining solutions is essential and highly meaningful to solve this problem. In real life, rain can be categorized into three types: rain streaks, raindrops, and rain mist 12 . Raindrops and rain mist usually appear as discrete points or blurry areas, with relatively simple textures and shapes. However, rain streaks exhibit complex textures and shapes, including variations such as heavy rain, light rain, and dense rain, which increases the difficulty of accurately identifying and separating them. Furthermore, rain streaks are typically continuous. Due to the complex interaction between rain streaks and the image content, as well as the variability and uncertainty associated with them, removing rain streaks from an image is more challenging compared to removing raindrops and fog. Therefore, we present a method for removing rain streaks from a single image in this paper.Single-image deraining methods aim to separate raindrops or rain streaks from the background scene. Rain layer and background layer modeling were key components of early model-driven methods for single-image rain removal. In these methods, such as sparse coding mode...

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Rain Streaks Removal in Image via Decomposition and Visibility Feature Saturation

Abstract: Abstract

Cited by 3 publications

References 15 publications

Comparative Analysis of Single Image Shadow Detection and Removal in Aerial Images

Comparative Analysis of Single Image Shadow Detection and Removal in Aerial Images

Modified Hue over Intensity Ratio Based Method for Shadow Detection and Removal in Arial Images

Single image deraining via wide rectangular regional blocks and dual attention complementary enhancement network

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