Multiplierless algorithms for high-speed real-time onboard image processing

This paper presents a new approach for restoring noisy images with a substantial number of missing samples. The system proposed is based on the linear prediction theory. The filters used are multiplierless since they have power-of-2 coefficients. This makes the algorithms fast and low cost for VLSI implementation. The system is composed of two stages. In the first one, the lost samples are recovered using the Least Mean Square (LMS)-like algorithm in which the missing samples are replaced by their estimates. In the second phase, noise is removed from the image using a genetic algorithm based linear predictor. This algorithm yields power-of-2 coefficients of the filter. The results are very promising and illustrate the performance of the multiplierless system.