A Review Paper on Real-Time Video Analysis in Dense Environment for Surveillance System

Tyagi, Himanshu; Kumar, Vivek; Kumar, Gaurav

doi:10.1109/icfirtp56122.2022.10059434

Cited by 3 publications

(1 citation statement)

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“…Rainfall is a prevalent meteorological phenomenon [1] that adversely affects the visual quality of images and hampers the performance of subsequent image processing tasks such as object recognition [2], object detection [3], autonomous driving, and video surveillance [4][5][6]. Consequently, the removal of rain streaks from rainy images has emerged as a significant and meaningful research topic, gaining attention in recent years.…”

Section: Introductionmentioning

confidence: 99%

R-PreNet: Deraining Network Based on Image Background Prior

Jiao,

Meng,

et al. 2023

Applied Sciences

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Single image deraining (SID) has shown its importance in many advanced computer vision tasks. Although many CNN-based image deraining methods have been proposed, how to effectively remove raindrops while maintaining background structure remains a challenge that needs to be overcome. Most of the deraining work focuses on removing rain streaks, but in heavy rain images, the dense accumulation of rainwater or the rain curtain effect significantly interferes with the effective removal of rain streaks, and often introduces some artifacts that make the scene more blurry. In this paper, a novel network architecture, R-PReNet, is introduced for single image denoising with an emphasis on preserving the background structure. The framework effectively exploits the cyclic recursive structure inherent in PReNet. Additionally, the residual channel prior (RCP) and feature fusion modules have been incorporated, enhancing denoising performance by emphasizing background feature information. Compared with the previous methods, this approach offers notable improvement in rainstorm images by reducing artifacts and restoring visual details.

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