Locally Adaptive DCT Filtering for Signal-Dependent Noise Removal

This work addresses the problem of signal dependent noise removal in images. An adaptive nonlinear filtering approach in the orthogonal transform domain is proposed and analyzed for several typical noise environments in the DCT domain. Being applied locally, i.e., within a window of small support, DCT is expected to approximate the Karhunen-Loeve decorrelating transform, which enables effective suppression of noise components. The detail preservation ability of the filter allowing not to destroy any useful content in images is especially emphasized and considered. A local adaptive DCT filtering for the two cases: when signal dependent noise can be and cannot be mapped into additive uncorrelated noise with homomorphic transform, is formulated. Although the main issue is signal dependent and pure multiplicative noise, the proposed filtering approach is also found to be competing with the state of the art methods on pure additive noise corrupted images.