Strategic image denoising using a support vector machine with seam energy and saliency features
Laura McCrackin, Shahram Shirani · 2014
We propose a method of using a support vector machine (SVM) to select between multiple well-performing contemporary denoising algorithms for each pixel of a noisy image. We describe a number of novel and pre-existing features based on seam energy, local colour, and saliency which are used as inputs to the SVM. Our SVM strategic image de-noising (SVMSID) results demonstrate better image quality than either candidate denoising algorithm, as measured using the perceptually-based quaternion structural similarity image metric (QSSIM).