Smart embedded system based on demosaicking for enhancement of surveillance systems. (September 2020)

• Record Type: Journal Article
• Authors: Din, Sadia; Paul, Anand; Ahmad, Awais
• Is Part Of: Computers & electrical engineering. Volume 86(2020)
• Publication Date: 2020-09
• Subjects: Smart cities -- Low power energy -- Demosaicking -- Cnn -- Noise -- Surveillance; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2020.106731
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Demosaicking and denoising are essential elements in digital photography pipelines. A novel demosaicking and denoising conjunct strategy using deep adaptive residual learning. Zero padding is performed to increase processing speed and preserve the edges of the image. Demosaicking using interpolation to find missing values. The reconstructed image is created using the original image. Abstract: Demosaicking and denoising are essential elements in digital photography pipelines. The use of convolutional neural networks (CNN)-based image demosaicking and denoising methods has been very successful. However, still there is a room for improvement in the network performance in terms of efficiency and accuracy. The main challenge that remains to be addressed is to guarantee the visual quality of reconstructed images, particularly in the presence of noise. To address these challenges, this paper introduces a novel demosaicking and denoising conjunct strategy using deep adaptive residual learning. The proposed framework has three stages. Initially, zero padding is performed to increase processing speed and preserve the edges of the image. In the second phase, we perform demosaicking using interpolation in order to find missing values using information about neighboring pixels. Finally, the reconstructed image is created using the original image. To evaluate the feasibility of the proposed scheme, we used Pytorch and Google Colab with 400 images for training and 100 images for validation The outcomes show that the proposed scheme beats cutting edge joint demosaicking and denoising schemes regarding both structural similarity index metrics (SSIM) and peak signal-to-noise ratio (PSNR) and basic similitude record measurements (SSIM). Graphical abstract: Image, graphical abstract