Image restoration by regularisation in uncorrelated transform domain

Abstract

This paper is a postprint of a paper submitted to and accepted for publication in IEE Proceedings-Vision, Image and Signal Processing and is subject to Institution of Engineering and Technology

Iterative Desensitisation of Image Restoration Filters under Wrong PSF and Noise Estimates

“…On the other hand, apart from the fixed error result of the original image estimation S xx | e ∼ = S yy , the parameter C e is distorted by changes in the variance of an estimated Gaussian noise. Expressly, we evaluate the variations of this parameter using the relative error of the standard deviation σ associated to the noise, namely, ε σ whose expression can be written using (35) as…”

“…Having a look to (8), we can verify the dependency of the new filter G on three basic parameters such as the original restoration filter G (e.g., the Wiener approach), the regularisation product GH e (different from the original regularisation GH) as explained in the restoration regularisation theory [33][34][35], and the number of iterations k of the model shown in Figure 1.…”

“…Nonetheless, in the restoration process, the parameter H e is deviated from its real value by adjusting both of its descriptive factors, namely, the number of moved pixels l and the inclination of the motion θ. As previously mentioned, the divergence of these parameters is expressed by means of the relative errors ε l and ε θ , respectively, whose definitions based on (35) as follow:…”