Compressive vector reconstruction: Hypothesis for blind image deconvolution

Alonso Orea Amador, University of Texas at El Paso

Abstract

Alternative imaging devices propose to acquire and compress images simultaneously. These devices are based on the compressive sensing (CS) theory. A reduction in the measurement required for reconstruction without a post-compression sub-system allows imaging devices to become simpler, smaller, and cheaper. In this research, we propose a new algorithm to compress and reconstruct blurred images for CS imaging devices. Blur effect in images is common due to relative motion, lens, limited aperture dimensions, lack of focus, and/or atmospheric turbulence. Our intention is to compress a blurred image with CS techniques and then reconstruct a blur-free version using the proposed algorithm. To assess the performance of the proposed algorithm in comparison to other CS based compression schemes, we have used the Peak-Signal-to-Noise-Ratio (PSNR). Our algorithm is based on the previous work of compressive blind image deconvolution (BID) [1] and in a new way of organizing wavelet coefficients [2]. We can see an improvement up to 2 dBs in the PSNR for the two highest compression rates comparing the proposed algorithm with the one presented in [1].^

Subject Area

Applied mathematics|Electrical engineering

Recommended Citation

Orea Amador, Alonso, "Compressive vector reconstruction: Hypothesis for blind image deconvolution" (2017). ETD Collection for University of Texas, El Paso. AAI10284578.
http://digitalcommons.utep.edu/dissertations/AAI10284578

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