Investigation of filtering of rain streaks affected video sequences under various quantisation parameter in HEVC encoder using an enhanced V‐BM4D algorithm

Jayaraman, Thiyagarajan; Chinnusamy, Gowri Shankar

doi:10.1049/iet-ipr.2018.6005

Cited by 5 publications

(2 citation statements)

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“…Rain images can also be regarded as a special image noise. e earliest research of the rain removal algorithm is to remove the rain in the video [4]. Due to the temporal and spatial relativity of the video sequence, image characteristics can be extracted from the video sequence of the same pixel at consecutive times to determine the position of the pixel affected by rain.…”

Section: Introductionmentioning

confidence: 99%

Research of Single Image Rain Removal Algorithm Based on LBP-CGAN Rain Generation Method

Xue

2021

Mathematical Problems in Engineering

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Rain has an undesirable negative effect on the clarity of the collected images. In situation where images are captured in rain, it can lead to a loss of information and disability in reflecting real images of the situation. Consequently, rain has become an obstacle in outdoor scientific research studies. The reason why images captured in rain are difficult to process is due to the indistinguishable interactions between the background features and rain textures. Since current image data are only processed with the CNN (convolutional neural network) model, a trained neural network to remove rain and obtain clear images, the resulted images are either insufficient or excessive from standard results. In order to achieve more ideal results of clearer images, series of additional methods are taken place. Firstly, the LBP (local binary pattern) method is used to extract the texture features of rain in the image. Then, the CGAN (conditional generative adversarial network) model is constructed to generate rain datasets according to the extracted rain characteristics. Finally, the existing clear images, rain datasets generated by CGAN, as well as the images with rain are used for convolution operation to remove rain from the images, and the average value of PSNR (peak signal to noise ratio) can reach 38.79 by using this algorithm. Moreover, a large number of experiments are done and have proven that this joint processing method is able to successfully and effectively generate clear images despite the rain.

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Section: Introductionmentioning

confidence: 99%

Research of Single Image Rain Removal Algorithm Based on LBP-CGAN Rain Generation Method

Xue

2021

Mathematical Problems in Engineering

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“…But they fail to provide satisfactory results in different realistic conditions like heavy rain streaks haze accumulated effect of rain patterns (Wang et al 2020b(Wang et al , 2020d). An enhanced video block matching four-dimensional (V-BM4D) deraining framework is proposed by Jayaraman and Chinnusamy (2020b) to remove the structural rain pattern from the successive video frames. Hence the rain pattern is predicted in the 4D domain.…”

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Performance analysis of a dual stage deep rain streak removal convolution neural network module with a modified deep residual dense network

Jayaraman,

Chinnusamy

2022

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The visual appearance of outdoor captured images is affected by various weather conditions, such as rain patterns, haze, fog and snow. The rain pattern creates more degradation in the visual quality of the image due to its physical structure compared with other weather conditions. Also, the rain pattern affects both foreground and background image information. The removal of rain patterns from a single image is a critical process, and more attention is given to remove the structural rain pattern from real-time rain images. In this paper, we analyze the single image deraining problem and present a solution using the dual stage deep rain streak removal convolutional neural network. The proposed single image deraining framework primarily consists of three main blocks: a derain streaks removal CNN (derain SRCNN), a modified residual dense block (MRDB), and a six-stage scale feature aggregation module (3SFAM). The ablation study is conducted to evaluate the performance of various modules available in the proposed deraining network. The robustness of the proposed deraining network is evaluated over the popular synthetic and real-time data sets using four performance metrics such as the peak signal-to-noise ratio (PSNR), the feature similarity index (FSIM), the structural similarity index measure (SSIM), and the universal image quality index (UIQI). The experimental results show that the proposed framework outperforms both synthetic and real-time images compared with other state-of-the-art single image deraining approaches. In addition, the proposed network takes less running and training time.

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Deep Video-Denoising Based on Multi-Scale Feature Extraction and Fusion Mechanism

Wang,

et al. 2023

2023 International Conference on Blockchain Technology and Applications (ICBTA)

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Investigation of filtering of rain streaks affected video sequences under various quantisation parameter in HEVC encoder using an enhanced V‐BM4D algorithm

Cited by 5 publications

References 21 publications

Research of Single Image Rain Removal Algorithm Based on LBP-CGAN Rain Generation Method

Research of Single Image Rain Removal Algorithm Based on LBP-CGAN Rain Generation Method

Performance analysis of a dual stage deep rain streak removal convolution neural network module with a modified deep residual dense network

Deep Video-Denoising Based on Multi-Scale Feature Extraction and Fusion Mechanism

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