Image enhancement based on signal subspace approach

A newly developed image enhancement algorithm is described in this contribution. The proposed algorithm makes use of the signal subspace method to enhance images corrupted by uncorrelated additive noise. This enhancement is performed by eliminating the noise subspace and estimating clean image from the remaining signal subspace. We propose the block-adaptive Wiener filtering which engages properties of the human visual system to estimate clean image. This criterion enables one to not only preserve the detailed structure of the given image, but to reduce the level of background noise as well. Subjective evaluation tests show the superiority of the method proposed here. In particular, edge blurring effects are noticeably reduced compared to the conventional methods.