Distributed approach for deblurring large images with shift-variant blur

Abstract

Abstract-Image deblurring techniques are effective tools to obtain high quality image from acquired image degraded by blur and noise. In applications such as astronomy and satellite imaging, size of acquired images can be extremely large (up to gigapixels) covering a wide field-of-view suffering from shiftvariant blur. Most of the existing deblurring techniques are designed to be cost effective on a centralized computing system having a shared memory and possibly multicore processor. The largest image they can handle is then conditioned by the memory capacity of the system. In this paper, we propose a distributed shift-variant image deblurring algorithm in which several connected processing units (each with reasonable computational resources) can deblur simultaneously different portions of a large image while maintaining a certain coherency among them to finally obtain a single crisp image. The proposed algorithm is based on a distributed Douglas-Rachford splitting algorithm with a specific structure of the penalty parameters used in the proximity operator. Numerical experiments show that the proposed algorithm produces images of similar quality as the existing centralized techniques while being distributed and being cost effective for extremely large images.

A Space-Variant Deblur Method for Focal-Plane Microwave Imaging

“…Camera [14] improved the sectioning approach and applied a deconvolution method with boundary effect correction in the segmentation algorithm and accelerated the method with scaled gradient projection (SGP). Mourya [15] proposed a distributed shift-variant image deblurring algorithm to solve the limited resource problem when the image is extremely large (up to gigapixels). Zhang [16] estimated the blur map and adopted the BM3D-based, non-blind deconvolution algorithm to reconstruct the image.…”