Abstract
We present a new approach to adaptive blind image deconvolution based on computational reinforced learning in attractor-embedded solution space. A new subspace optimization technique is developed to restore the image and identify the blur. Conjugate gradient optimization is employed to provide an adaptive image restoration while a new evolutionary scheme is devised to generate the high-performance blur estimates. The new technique is flexible as it does not suffer from various image or blur constraints imposed by most traditional blind methods. Experimental results show that the new algorithm is effective in blind deconvolution of images degraded under different blur structures and noise levels.
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