Date of Award


Degree Name

Master of Science


Electrical Engineering


Bryan Usevitch


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].




Received from ProQuest

File Size

69 pages

File Format


Rights Holder

Alonso Orea Amador