A neural network for deblurring an image
C.M. Jubien, M.R. Jernigan · 2002
A neural network architecture for deblurring a blurry scene without prior knowledge of the blur is proposed. Two different training algorithms are described, one a standard neural network training algorithm (employing the least mean squares (LMS) rule) and the second an original algorithm, dubbed algorithm-X. Both were successful for developing inverse blur filters to enhance a blurry picture. Algorithm-X is computationally less complex than the LMS algorithm, and in tests comparing the training times of the two algorithms, algorithm-X was found to be faster.>