Signal Processing Lab. 신호처리연구실

When we take an image with a camera, a microscope, or a telescope, we want to acquire a faithful representation of a scene, specimen, or star. But every image suffers from some form of degradation. A lens collects and focuses light coming from all directions on a small sensor. The light smears out even with an expensive lens focused on a still object, even more so when the light travels through nonuniform media and when focused on an object is in motion. We may reduce degradation by employing complicated and expensive equipment with the lens consisting of multiple groups and elements, mechanical stabilization, etc. We can restore a faithful original representation from an observed image via signal processing if we know the degradation introduced during the acquisition process. where g, f , and n are vectors that represent observed, original, and noise images, respectively. H is a matrix representing the degradation of the acquisition process. The image restoration is an inverse problem of finding f from g. When the degradation does not change on a sensor, the degradation is modeled with a circulant matrix that can be diagonalized by a discrete Fourier transform (DFT). Once we write the process with a diagonal matrix, the analysis for restoration is straightforward, and the implementation of restoration is efficient. Most of the successful restoration methods rely on the diagonalization of H by the DFT. However, the degradation in real life often changes with the locations on a sensor. Our research focuses on the modeling and restoration of nonstationary degradation. Deep neural networks have become an essential tool for image processing. Conventional approaches to signal processing usually start with a domain expert analyzing data characteristics to find a good represen