Real-time implementation of large-size image restoration with edge-preserving

Up to now, there are a large number of image restoration algorithms developed and implemented on power-efficient hardware platforms. In this paper a fast restoration approach based on edge-preserving is proposed and ported on a multi- DSP platform. Firstly, classical Wiener filter is optimized and the blurred image is decomposed into two independent parts in frequency domain: a shift-invariant part and a shift-variant part. Then the result is obtained by combining the two parts. Secondly, parallel processing is adopted for the large volumes of data and the complex algorithms commonly encountered. The algorithm mentioned above is realized on a parallel system with 4 DSPs because one DSP can not afford such large amounts of data. In order to make full use of processors and memory, the timing and throughput is designed carefully to guarantee the data processed in a pipeline manner. In a word, experiments show the algorithm has higher performance than other methods with the same computational complexity and can achieve real-time processing.