A comparative analysis of denoising algorithms for extragalactic imaging surveys

We present a comprehensive analysis of the performance of noise-reduction (``denoising'') algorithms to determine whether they provide advantages in source detection on extragalactic survey images. The methods under analysis are Perona-Malik filtering, Bilateral filter, Total Variation denoising, Structure-texture image decomposition, Non-local means, Wavelets, and Block-matching. We tested the algorithms on simulated images of extragalactic fields with resolution and depth typical of the Hubble, Spitzer, and Euclid Space Telescopes, and of ground-based instruments. After choosing their best internal parameters configuration, we assess their performance as a function of resolution, background level, and image type, also testing their ability to preserve the objects fluxes and shapes. We analyze in terms of completeness and purity the catalogs extracted after applying denoising algorithms on a simulated Euclid Wide Survey VIS image, on real H160 (HST) and K-band (HAWK-I) observations of the CANDELS GOODS-South field. Denoising algorithms often outperform 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 Bilateral filtering on the Euclid-VIS image, we obtain catalogs that are both more pure and complete by 0.2 magnitudes than those based on the standard approach. The same result is achieved with the Structure-Texture image decomposition algorithm applied on the H160 image. The advantage of denoising techniques with respect to PSF filtering increases at increasing depth. Moreover, these techniques better preserve the shape of the detected objects with respect to PSF smoothing. Denoising algorithms provide significant improvements in the detection of faint objects and enhance the scientific return of current and future extragalactic surveys.