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This paper presents an image denoising algorithm, which applies bilateral filtering (BLF) in the Laplacian subbands. It is noted that the subband images have wider area of photometric similarity than the original, and hence, they can be more benefited by the BLF than the original. Specifically, an image is Gaussian filtered to obtain a low band image, and the low band image is subtracted from the original to have the high band signal, which forms the Laplacian subbands. For the high band image denoising, we derive an adaptive kernel that is dependent on the edge intensity and photometric similarity of subband images. The high band image is convolved with this kernel and then added to the denoised low band signal, which produces the denoised image. We also propose to process the denoised high band signal by the gradient histogram preservation method, for sharpening the edges with less noise amplification. Experimental results show that the proposed denoising method provides higher PSNR than the original BLF and other multi-resolution denoising algorithms. Since the high band image is also effectively denoised in this process, the sharpened image by high band modification is also visually more pleasing when compared with the results of the conventional sharpening methods.

Bilateral image denoising in the Laplacian subbands