Abstract
This paper presents a novel multichannel recursive filtering (MRF) technique to address blind image restoration. The primary motivation for developing the MRF algorithm to solve multichannel restoration is due to its fast convergence in joint blur identification and image restoration. The estimated image is recursively updated from its previous estimates using a regularization framework. The multichannel blurs are identified iteratively using conjugate gradient optimization. The proposed algorithm incorporates a forgetting factor to discard the old unreliable estimates, hence achieving better convergence performance. A key feature of the method is its computational simplicity and efficiency. This allows the method to be adopted readily in real-life applications. Experimental results show that it is effective in performing blind multichannel blind restoration.
Highlights
Image restoration deals with the estimation of the original images from the observed blurred, degraded images using the partial information about the imaging system
The contributions of the proposed technique, include the following. (i) As opposed to other multichannel restoration algorithms, it does not require all the data to be available simultaneously as recursive filtering updates the estimate based on first-come-first-served basis. (ii) All the operations of multichannel recursive filtering (MRF) for image-domain minimization are conducted in the frequency domain through discrete Fourier transform (DFT), efficiently reduce the computational cost. (iii) It incorporates a forgetting factor to discard the old unreliable estimates, achieving better convergence performance
This is supported by objective performance measure as our method offers peak signal-to-noise ratio (PSNR) of 29.41 dB, as opposed to 28.70 dB, 26.10 dB, and 25.85 dB by the Conjugate gradient optimization (CGO)-alternating minimization (AM), Total variation (TV)-AM, and WF-AM methods, respectively
Summary
Image restoration deals with the estimation of the original images from the observed blurred, degraded images using the partial information about the imaging system. With the assumption that the multichannel PSFs are weakly coprime, and in the absence of noise, the desired image and PSFs can be transformed into the null space of a special matrix constructed from the degraded images [3,4,5,6] Centered on this idea, several techniques have been proposed which include greatest common divisor (GCD) [3], subspace-based [4, 5], and eigenstructure-based approaches [6]. Subspace-based methods work by first estimating the blurring function using a procedure of min-eigenvector, followed by conventional image restoration using the identified PSFs. In similar concept, eigenstructure-based algorithm transforms the null space problem into a constrained optimization framework and performs direct deconvolver estimation. The basic idea is focused on Wiener filtering of the observed degraded images, and updating the filters using a nonlinear Bayesian estimation of the estimated image Speaking, these iterative methods are extensions of EURASIP Journal on Advances in Signal Processing single-channel blind image restorations approaches.
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