New wavelet techniques are employed to improve the perceptual quality of images, and to enhance and detect the detail features in the region of interest (ROI). Distributed approximating functionals (DAFs) are used to construct a new class of interpolating wavelets, which enable better image processing performance. This paper is focused on recent improvements in DAF wavelet image processing. The combined perceptual techniques (such as visual group normalization and contrast nonlinear enhancement) produce natural high-quality images adapted to the human vision system. The underlying technologies significantly facilitate the creation of generic image processing and computer-aided diagnostic (CAD) systems.

A weight function is introduced to the generalized version of the alpha-trimmed mean filter for the removal of impulse noise from corrupted images. Iterative implementation of the new filter-based switching scheme is proposed as an impulse detector to preserve the noisefree pixels.Numerical simulations show that the proposed filter is quite robust and efficient, and its performance compares favorably with many other wellknown nonlinear filtering algorithms.