Spatial-Frequency Domain Nonlocal Total Variation for Image Denoising
This is an incremental improvement for image processing researchers, offering a new reference algorithm in the family of TV and non-local operator methods.
The paper tackles image denoising by proposing a Local Spatial-Frequency Nonlocal Total Variation (L-SFNLTV) model, which combines spatial and Fourier domain processing to improve performance, resulting in enhanced image quality and computational speed compared to existing methods.
Following the pioneering works of Rudin, Osher and Fatemi on total variation (TV) and of Buades, Coll and Morel on non-local means (NL-means), the last decade has seen a large number of denoising methods mixing these two approaches, starting with the nonlocal total variation (NLTV) model. The present article proposes an analysis of the NLTV model for image denoising as well as a number of improvements, the most important of which being to apply the denoising both in the space domain and in the Fourier domain, in order to exploit the complementarity of the representation of image data in both domains. A local version obtained by a regionwise implementation followed by an aggregation process, called Local Spatial-Frequency NLTV (L- SFNLTV) model, is finally proposed as a new reference algorithm for image denoising among the family of approaches mixing TV and NL operators. The experiments show the great performance of L-SFNLTV, both in terms of image quality and of computational speed, comparing with other recently proposed NLTV-related methods.