Astronomical Image Denoising Using Dictionary Learning

Abstract

Astronomical images suffer a constant presence of multiple defects that are consequences of the intrinsic properties of the acquisition equipments, and atmospheric conditions. One of the most frequent defects in astronomical imaging is the presence of additive noise which makes a denoising step mandatory before processing data. During the last decade, a particular modeling scheme, based on sparse representations, has drawn the attention of an ever growing community of researchers. Sparse representations offer a promising framework to many image and signal processing tasks, especially denoising and restoration applications. At first, the harmonics, wavelets, and similar bases and overcomplete representations have been considered as candidate domains to seek the sparsest representation. A new generation of algorithms, based on data-driven dictionaries, evolved rapidly and compete now with the off-the-shelf fixed dictionaries. While designing a dictionary beforehand leans on a guess of the most appropriate representative elementary forms and functions, the dictionary learning framework offers to construct the dictionary upon the data themselves, which provides us with a more flexible setup to sparse modeling and allows to build more sophisticated dictionaries. In this paper, we introduce the Centered Dictionary Learning (CDL) method and we study its performances for astronomical image denoising. We show how CDL outperforms wavelet or classic dictionary learning denoising techniques on astronomical images, and we give a comparison of the effect of these different algorithms on the photometry of the denoised images.

References in corpus (4)

• Fermi Large Area Telescope Second Source Catalog
• The XMM Large Scale Structure survey: The X-ray pipeline and survey selection function
• SZ and CMB reconstruction using Generalized Morphological Component Analysis
• Sparse component separation for accurate CMB map estimation

Cited by in corpus (10)

• On the Computational Intractability of Exact and Approximate Dictionary Learning
• Learning to Denoise Astronomical Images with U-nets
• High Resolution Weak Lensing Mass-Mapping Combining Shear and Flexion
• A PCA-based automated finder for galaxy-scale strong lenses
• Super-resolution method using sparse regularization for point-spread function recovery
• A comparative analysis of denoising algorithms for extragalactic imaging surveys
• Fast Orthonormal Sparsifying Transforms Based on Householder Reflectors
• SNIa detection in the SNLS photometric analysis using Morphological Component Analysis
• Semi-supervised dictionary learning with graph regularization and active points
• Learning sparse representations on the sphere