Image denoising by directional averaging of wavelet coefficients
Bruno Huysmans, Alexandra Pižurica, Wilfried R. Philips · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
In this paper a new denoising technique for gray valued images is presented. The proposed technique is best suited for flat or textured images affected by relatively low noise levels, where we aim at high quality reconstruction of tiny image structures and fine details. To avoid the attenuation of these fine image details, we replace the common wavelet thresholding and shrinking rules by an averaging step over a certain region of consistent edge directions. This region is obtained by first extracting the pixels that belong to an "oriented structure". We develop a classification algorithm which extracts the oriented structures by using directional information from the wavelet detail images. After this classification step we perform an adaptive averaging: each pixel is averaged over a window that depends on the detected structures in its neighbourhood. We demonstrate the visual improvement of our method over two spatially adaptive wavelet shrinkage methods.