Typical high-abundant proteins, including albumin, IgG, IgA and others, are the target of depletion methods usually applied to two-dimensional electrophoresis (2DE) of human biological fluids like serum and plasma. Detection of low-abundant proteins is of interest with regard to biomarkers for disease when being studied by 2DE or liquid chromatography-mass spectrometry (LC/MS). After depletion of very abundant proteins, serum samples consist of an enriched pool of low-abundant proteins that can be further studied without significant interferences, thus allowing for a full identification of the low abundant proteins, whose spots become now more visible. We have employed wavelet-based techniques and their derived denoisers to explore 2DE from disease-control human samples. We have pursued the goal of mimicking in silico the spot detection performance experimentally obtained by depletion methods, thus hoping to read through the critical high-abundant protein regions. Our results suggest that an efficient and effective computational tool has been added to other ones performing 2DE image analysis, such as decomposition and segmentation, but with the advantage of being specifically targeted to the depletion task.