Joint denoising and decompressionPP

Wavelet compression is a well-settled technique in image pipelines (such as satellite imagery and JPEG2000) and the quantification of noisy coefficients often causes outliers, which result in highly-correlated artifacts in the spatial domain. We propose a joint restoration scheme that uses a precise degradation model taking into account the intertwined effects of noise and compression. Different kinds of regularization, including a novel Bayesian patch-based approach, Gaussian Mixture Models and Convolutional Neural Networks are explored.

This is poster number 53 in Poster Session

Mario Gonzalez (Universidad de la República (Uruguay), Université Paris Descartes)
Andrés Almansa (MAP5 - CNRS - Université Paris Descartes)
Pablo Musé (Facultad de Ingeniería, Universidad de la República)