21 May 2004 Restoration of images with optical aberrations and quantization in a transform domain
Author Affiliations +
Digital images generally suffer from two main sources of degradations. The first includes errors introduced in imaging, such as blurring due to optical aberrations and sensor noise. The second includes errors introduced during the processing. One particular example is the quantization noise arising from lossy compression. While image restoration is concerned with the recovery of the object from these degradations, often we only deal with one type of the error at a time. In this paper, we present a restoration algorithm that handles images with optical aberrations and quantization in a transform domain. We show that it can be cast in a joint optimization setting, and demonstrate how it can be solved efficiently through alternating minimization. We also prove analytically that the algorithm is globally convergent to a unique solution when the restoration uses either H1-norm or TV-norm regularization. Simulation result asserts that this joint minimization produces images with smaller relative errors compared to a standard regularization model.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Edmund Y. Lam, Edmund Y. Lam, Michael K. Ng, Michael K. Ng, } "Restoration of images with optical aberrations and quantization in a transform domain", Proc. SPIE 5299, Computational Imaging II, (21 May 2004); doi: 10.1117/12.525283; https://doi.org/10.1117/12.525283


Video imagers with low speed CCD and LC based on...
Proceedings of SPIE (August 09 2015)
Optical preconditioning and digital image restoration
Proceedings of SPIE (August 22 2005)
Derivate statistics for Karhunen-Loève transform
Proceedings of SPIE (April 17 2006)
Face recognition via a projective compressive sensing system
Proceedings of SPIE (February 13 2012)
Spatio-temporal sampling for video
Proceedings of SPIE (September 04 2008)

Back to Top