Strategic Image Denoising Using a Support Vector Machine with Seam Energy and Saliency Features

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 denoising (SVMSID) results demonstrate better image quality than either candidate denoising algorithm, as measured using the perceptually-based quaternion structural similarity image metric (QSSIM).