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