Authors:
Hongwei Zheng
and
Olaf Hellwich
Affiliation:
Computer Vision & Remote Sensing, Berlin University of Technology, Germany
Keyword(s):
Bayesian estimation, regularization, convex optimization, functions of bounded variation, linear growth functional,
self-adjusting, parameter estimation, data-driven, hyperbolic conservation laws, image restoration.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Early Vision and Image Representation
;
Enhancement and Restoration
;
Image and Video Analysis
;
Image Filtering
;
Image Formation and Preprocessing
;
Image Quality
;
Implementation of Image and Video Processing Systems
;
Mathematical Morphology
;
Statistical Approach
Abstract:
We present a novel variational regularization in the space of functions of Bounded Variation (BV) for adaptive data-driven image restoration. The discontinuities are important features in image processing. The BV space is well adapted for the measure of gradient and discontinuities. More over, the degradation of images includes not only random noises but also multiplicative, spatial degradations, i.e., blur. To achieve simultaneous image deblurring and denoising, a variable exponent linear growth functional on the BV space is extended in Bayesian estimation with respect to deblurring and denoising. The selection of regularization parameters is self-adjusting based on spatially local variances. Simultaneously, the linear and non-linear smoothing operators are continuously changed following the strength of discontinuities. The time of stopping the process is optimally determined by measuring the signal-to-noise ratio. The algorithm is robust in that it can handle images that are formed
with different types of noises and blur. Numerical experiments show that the algorithm achieves more encouraging perceptual image restoration results.
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