Adaptive smoothing and edge tracking in image deblurring and denoising

Tonazzini A.

Image processing  Image denoising  Image processing and computer vision 

Image deblurring and denoising are formulated as the minimization of an energy function in which a line process is implicity referred through a novel discontinuity-adaptive stabilizer. This stabilizer depends on a parameter, called temperature, which is related to the threshold for the creation of intensity discontinuities (edges). The solution is computed using a GNC-like algorithm that minimizes in sequence the energy function at decreasing values of the temperature. We show that this allows for a coarse-to-fine recovery of edges of decreasing width, while smoothing off the noise. Furthermore, the need for a fine tuning of the regularization an threshold parameters is significantly relaxed. As a further advantage with respect to the most edge-preserving stabilizers, the method is also flexible for the introduction of self-interactions between lines, in order to express various constraints on the configurations of the edge field, without any increase in the computational cost.

Source: Pattern recognition and image analysis 10 (2000): 492–499.

Publisher: Distributed by Allen Press,, Lawrence, KS , Stati Uniti d'America