Image deblurring using a pyramid-based Richardson-Lucy algorithm

In image deblurring, it is important to reconstruct images with small error, high perception quality, and less computational time. In this paper, a blurred image reconstruction algorithm, which is a combination of the Richardson-Lucy (RL) deconvolution approach and a pyramid structure, is proposed. The RL approach has good performance in image reconstruction. However, it requires an iterative process, which costs a lot of computation time, and the reconstructed image may suffer from a ringing effect.

In the proposed algorithm, we decompose a blurred image from a coarse scale to a fine scale and progressively utilize the RL approach with different number of iterations for each scale. Since the number of iterations is smaller for the large scale part, the computation time can be reduced and the ringing effect caused from details can be avoided. Simulation results show that our proposed algorithm requires less computation time and has good performance in blurred image reconstruction.

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