Another field in our research in DSPLab is in Speech Quality Enhancement. In addition to classical well-known methods (such as Spectral Subtraction and Wiener Filter), we now are working on statistical approach in speech enhancement in both trained and none-trained manners.

Image enhancement tools are often classified into point operations and spatial operations. Point operations include contrast stretching, noise clipping, histogram modification, and pseudo-coloring. Point operations are, in general, simple nonlinear operations that are well known in the image processing literature and are covered elsewhere. Spatial operations used in image processing today are, on the other hand, typically linear operations. The reason for this is that spatial linear operations are simple and easily implemented. Although linear image enhancement tools are often adequate in many applications, significant advantages in image enhancement can be attained if nonlinear techniques are applied. Nonlinear methods effectively preserve edges and details of images while methods using linear operators tend to blur and distort them. Additionally, nonlinear image enhancement tools are less susceptible to noise. Noise is always present due to the physical randomness of image acquisition systems. For example, under-exposure and low-light conditions in analog photography conditions lead to images with film-grain noise, which, together with the image signal itself, are captured during the digitization process.