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We present a comprehensive analysis of the performance of noise-reduction(``denoising'') algorithms to determine whether they provide advantages insource detection on extragalactic survey images. The methods under analysis arePerona-Malik filtering, Bilateral filter, Total Variation denoising,Structure-texture image decomposition, Non-local means, Wavelets, andBlock-matching. We tested the algorithms on simulated images of extragalacticfields with resolution and depth typical of the Hubble, Spitzer, and EuclidSpace Telescopes, and of ground-based instruments. After choosing their bestinternal parameters configuration, we assess their performance as a function ofresolution, background level, and image type, also testing their ability topreserve the objects fluxes and shapes. We analyze in terms of completeness andpurity the catalogs extracted after applying denoising algorithms on asimulated Euclid Wide Survey VIS image, on real H160 (HST) and K-band (HAWK-I)observations of the CANDELS GOODS-South field. Denoising algorithms oftenoutperform the standard approach of filtering with the Point Spread Function(PSF) of the image. Applying Structure-Texture image decomposition,Perona-Malik filtering, the Total Variation method by Chambolle, and Bilateralfiltering on the Euclid-VIS image, we obtain catalogs that are both more pureand complete by 0.2 magnitudes than those based on the standard approach. Thesame result is achieved with the Structure-Texture image decompositionalgorithm applied on the H160 image. The advantage of denoising techniques withrespect to PSF filtering increases at increasing depth. Moreover, thesetechniques better preserve the shape of the detected objects with respect toPSF smoothing. Denoising algorithms provide significant improvements in thedetection of faint objects and enhance the scientific return of current andfuture extragalactic surveys.

A comparative analysis of denoising algorithms for extragalactic imaging surveys