Débruitage d’image par fusion de filtrage spatio-fréquentielle
In this work, we are interested in the fusion of two different algorithms of denoising and operating in the space domain for one of the two and in the frequency domain for the other one. The proposed technique will merge (fuse) the essential characteristics of these two algorithms. One of them is based on the fact that any natural image inherently exhibits spatial self-similarities (Non-Local means algorithm) and this property turns out to be especially efficient for denoising the contours, details and the spatial high frequencies of an image. The other algorithm is based on the fact that the image/signal is sparse and by this fact, each part of the image can be broken into a small number of periodic elementary functions (via the Discrete Cosine Transform). This latter denoising algorithm has proven to be especially effective for denoising homogeneous regions existing in a natural image. The image, degraded by a Gaussian additive noise is first denoised in both domains and segmented for contour detection. The three estimates are then fused to obtain the final denoised image. The article [15] and the article [73] will allow us to detect contours. Our experiments give good results of denoising. The evaluation of these results, based on the visual aspect, the mean squared error (MSE) and the signal-to-noise ratio (PSNR), demonstrates the relevance and effectiveness of the approach. This technique gives better results than those of [11] and [55, 78].
restauration image processing denoising filtering restoration noise reduction