Abstract :
Image denoising is an important image processing task, both as a process itself, and as a component in other processes. Very many ways to denoise an image or a set of data exists. The main properties of a good image denoising model are that it will remove noise while preserving edges. Traditionally, linear models have been used.
One common approach is to use a Gaussian filter, or equivalently solving the heat-equation with the noisy image
as input-data, i.e. a linear, 2nd order PDE-model. For some purposes this kind of denoising is adequate. One big advantage of linear noise removal models is the speed. But a back draw of the linear models is that they are not able to preserve edges in a good manner: edges, which are recognized as discontinuities in the image, are smeared
out. Here I am using a novel approach to image denoising that is level set approach is employed. Level Set Methods offer an appealing approach to noise removal. In particular, they exploit the fact that curves moving under their curvature smooth out and disappear. Since the method evolves contours, boundaries remain essentially sharp and do not blur. Second, a "min/max" switch is used to control whether or not curvature flow is applied; this results in an algorithm that stops automatically once the smallest features are removed.
Keyword :
Gaussian denoising, single image super-resolution (SISR) and JPEG image deblocking,DnCNN, AWGN