On Adapting the Tensor Voting Framework to Robust Color Image Denoising

Abstract

AbstractThis paper presents an adaptation of the tensor voting framework for color image denoising, while preserving edges. Tensors are used in order to encode the CIELAB color channels, the uniformity and the edginess of image pixels. A specific voting process is proposed in order to propagate color from a pixel to its neighbors by considering the distance between pixels, the perceptual color difference (by using an optimized version of CIEDE2000), a uniformity measurement and the likelihood of the pixels being impulse noise. The original colors are corrected with those encoded by the tensors obtained after the voting process. Peak to noise ratios and visual inspection show that the proposed methodology has a better performance than state-of-the-art techniques.KeywordsColor ImageImpulse NoiseImage DenoisingPerceptual ColorUniformity MeasurementThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.