Power-of-2 quantized algorithms for recovering lost samples from images.

摘要

This thesis presents new approaches for restoring noisy images with a substantial number of missing samples. The algorithms proposed are based on the linear prediction theory and are derived from the Least Mean Square (LMS) and the Euclidean Direction Search (EDS) algorithms. These algorithms are multiplier free, that is, all filters have power-of-2 coefficients. This makes the algorithms fast and low cost for VLSI implementation. The steady-state behavior of the output mean squared error of the finite-precision Power-of-2 Quantized LMS algorithm is analyzed. The algorithms developed in this thesis are not only tested in image restoration, but also in channel equalization. The results are very promising and illustrate the performance of the multiplier-free algorithms.