Fast GPU-based denoising filter using isoline levels

In this study, we describe a GPU-based filter for image denoising, whose principle rests on Matheron’s level sets theory first introduced in 1975 but rarely implemented because of its high computation cost. We use the fact that, within a natural image, significant contours of objects coincide with parts of the image level-lines. The presented algorithm assumes an a priori knowledge of the corrupting noise type and uses the polygonal level-line modeling constraint to estimate the gray-level of each pixel of the denoised image by local maximum likelihood optimization. Over the 512 × 512 pixel test images, the freely available implementation of the state-of-the-art BM3D algorithm achieves 9.56 dB and 36 % of mean improvement in 4.3 s, respectively, for peak signal to noise ratio and mean structural similarity index. Over the same images, our implementation features a high quality/runtime ratio, with a mean improvement of 7.14 dB and 30 % in 9 ms, which is 470 times as fast and potentially allows processing high-definition video images at 19 fps.