Abstract

In this paper we propose an approach for handling noise in deconvolution algorithm based on multidirectional filters. Most image deconvolution techniques are sensitive to the noise. Even a small amount of noise will degrade the quality of image estimation dramatically. We found that by applying a directional low-pass filter to the blurred image, we can reduce the noise level while preserving the blur information in the orthogonal direction to the filter. So we apply a series of directional filters at different orientations to the blurred image, and a guided filter based edge-preserving image deconvolution is used to estimate an accurate Radon transform of the clear image from each filtered image. Finally, we reconstruct the original image using the inverse Radon transform. We compare our deconvolution algorithm with many competitive deconvolution techniques in terms of the improvement in signal-to-noise ratio and visual quality.

© 2013 Optical Society of America

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