Optimization assisted autoregressive technique with deep convolution neural network-based entropy filter for image demosaicing

Abstract

ABSTRACTThis paper presents an image demosaicing based on an optimization-driven deep learning model, namely the Autoregressive Water Wave Optimization algorithm (Autoregressive-WWO). The proposed method is devised by assimilating the Wave Optimization algorithm (WWO), and the Conditional autoregressive value at risk (CAViaR) model. Here, the input images are subjected to Autoregressive WWO-based local polynomial approximation and intersection of confidence intervals (LPA-ICI) filter, and Deep Convolution neural network (Deep CNN) in a concurrent manner. The filter coefficients are obtained from the proposed Autoregressive WWO-based LPA-ICI filter and the residual image is obtained from Deep CNN. In order to create the demosaiced image, these two outputs are combined using an entropy measure. The proposed method offered superior performance with the highest Peak signal to noise ratio (PSNR) of 40.049dB, the highest Second derivative measure of enhancement (SDME) of 50.168dB, and highest Structural Index Similarity (SSIM) of 0.9056.KEYWORDS: Image demosaicingentropycolour filter arraydeep convolution neural networkfusion processLPA-ICI filterWWOCAViaR AcknowledgementsI would like to convey my sincere gratitude to the co-authors of this publication for their insightful advice and support throughout the conception and planning of this research project. All authors have made substantial contributions to conception and design, revising the manuscript, and the final approval of the version to be published. Also, all authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementMultispectral Image Database: Stuff, ‘https://www.cs.columbia.edu/CAVE/databases/multispectral/stuff/’ Accessed on April 2021.