LAUSR

Image denoising is a well explored but still an active research topic. The focus is usually on achieving higher numerical quality which is theoretically interesting, however, often the factor of computation cost is not considered. Our idea is to employ different image Gaussian noise filters to construct an effective image denoiser, where the deficiency of each filter is compensated with others, while a wide variation of quality versus speed can be achieved. We integrate filters using different cascaded forms and show that if two filters use uncorrelated features, their cascaded form provides a higher quality than each separately. We start with easy-to-implement filters employing pixel- and frequency-domain with different kernel size to construct a fast yet high-quality multi-domain denoiser. Then, we propose more complex denoisers by integrating our cascaded multi-domain denoiser to other state-of-the-art denoising methods. Simulations show that the quality of proposed multi-domain denoiser is significantly higher than its building-blocks. We also show that the proposed multi-domain denoiser can be integrated to state-of-the-art denoisers to from a more effective denoiser, while adding negligible complexity.