We address the problem of transform domain image de- noising in parts. Considering additive data independent noise corrupted images in the first part, we review a class of local trans- form domain filters, and compare their performances. We improve the performance of local transform domain filters by proposing av- eraging over overlapping windows. Comparisons include discussion of relation with wavelet denoising and simulations over different images. In the second part, we consider data dependent noise cor- ruptec images, and propose a novel transform domain denoising method. We study the performance of the method for the method for the case of film-grain noise. Experimental results justify the effectiveness of the studied transform domain filters.
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