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

C. Anitha Mary, A. Boyed Wesley
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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.
基于深度卷积神经网络熵滤波的图像去马赛克优化辅助自回归技术
摘要提出了一种基于优化驱动深度学习模型的图像去马赛克算法,即自回归水波优化算法(Autoregressive Water Wave Optimization algorithm,简称Autoregressive- wwo)。该方法是将波浪优化算法(WWO)和条件自回归风险值(CAViaR)模型相结合而设计的。在这里,输入图像以并行的方式进行基于自回归的局部多项式近似和置信区间相交(LPA-ICI)滤波器和深度卷积神经网络(Deep CNN)。滤波器系数由提出的自回归基于ww的LPA-ICI滤波器获得,残差图像由Deep CNN获得。为了创建去马赛克图像,使用熵度量将这两个输出组合在一起。该方法的峰值信噪比(PSNR)最高为40.049dB,二阶导数增强(SDME)最高为50.168dB,结构指数相似度(SSIM)最高为0.9056。关键词:图像去拼接熵彩色滤波器阵列深度卷积神经网络融合过程lpa - ici滤波器wwocaviar致谢我想向这篇文章的合著者表示诚挚的感谢,感谢他们在整个研究项目的构思和规划过程中提供的富有洞察力的建议和支持。所有作者在构思设计、修改稿件、最终审定出版版本等方面都做出了实质性的贡献。此外,所有作者同意对工作的各个方面负责,以确保与工作任何部分的准确性或完整性有关的问题得到适当的调查和解决。披露声明作者未报告潜在的利益冲突。数据可用性声明多光谱图像数据库:Stuff, ' https://www.cs.columbia.edu/CAVE/databases/multispectral/stuff/ '于2021年4月访问。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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