Towards A Unified View of Unsupervised Non-Local Methods For Image Denoising: The Nl-Ridge Approach

The goal of this work is to propose a simple yet efficient way to dynamically transform a sequence of images according to the functional properties of the visual system. To achieve this goal, we extend to video sequences the Retina-inspired Filter (RIF), which we have recently proposed for still images. Under the assumption that the input signal remains constant for a given time, the RIF decomposition was proven to be invertible, meaning that the image could be perfectly recovered. in this paper, we relax this assumption into a piece-wise constant input and we prove that RIF can be applied to a Group of Pictures (GOP). Under the condition that a GOP consists of frames without strong pixel motion, we mathematically prove and experimentally show that when RIF is applied to GOP, whatever the size of the GOP is, we are still able to perfectly recover the video frames and at the same time simplify the complexity of the whole process. in addition, we show that while the GOP size increases, the memory cost required to store this amount of frames is sufficiently reduced.