On Selecting the Appropriate Scale in Image Selective Smoothing by Nonlinear Diffusion

Image denoising and selective smoothing are important research problems in the area of image processing and computer vision. Partial differential equation (PDE) model filters were widely utilized due to their robust anisotropic diffusion properties that preserve edges. Spatial regularization via Gaussian low-pass filtering is used in well posed anisotropic diffusion PDE for image restoration that involves a crucial scale parameter. In this work, we provide an appropriate scale selection approach that obtains improved selective smoothing with nonlinear diffusion. Experimental results indicate the promise of such a strategy on a variety of synthetic and real noisy images. Further, compared to other diffusion PDE models the proposed technique improves the quality of final denoised images in terms of higher peak signal to noise ratio and structural similarity.