A pipeline to improve compressed image quality

Jean-Marc Delvit; Carole Thiebaut; Christophe Latry; Gwendoline Blanchet; Roberto Camarero

doi:10.1117/12.2536189

12 July 2019 A pipeline to improve compressed image quality

Jean-Marc Delvit, Carole Thiebaut, Christophe Latry, Gwendoline Blanchet, Roberto Camarero

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Proceedings Volume 11180, International Conference on Space Optics — ICSO 2018; 111807I (2019) https://doi.org/10.1117/12.2536189
Event: International Conference on Space Optics - ICSO 2018, 2018, Chania, Greece

Abstract

This paper presents a new image restoration pipeline performing especially well on noisy and compressed images. Most images are corrupted by noise. The signal to noise ratio (SNR) level increases with the pixel intensity value, which makes the denoising process especially challenging in dark areas of the images. Moreover, these areas are more likely to be highly compressed since they have low signal variations.

In this paper, we take into account compression by introducing a pre-processing step restituting the instrument noise. Then we propose a denoising and deconvolution step optimally parametrized since the instrument response (noise and Modulation Transfer Function) is known. We achieve better restoration than classical algorithms on satellite imagery. This improvement in image quality is shown on two kinds of application: pansharpening and 3D restitution.

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Jean-Marc Delvit, Carole Thiebaut, Christophe Latry, Gwendoline Blanchet, and Roberto Camarero "A pipeline to improve compressed image quality", Proc. SPIE 11180, International Conference on Space Optics — ICSO 2018, 111807I (12 July 2019); https://doi.org/10.1117/12.2536189

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