Iterative Video De-blurring Algorithm Utilizing a Neighborhood of Unblurred Frames

In this proposed work, video deblurring can be done by iterative operations on blurred frames using Accurate Blur
Kernel estimation and residual deconvolution processes. In general, while recording a video sequence using a digital camera or
a digital camcorder, blurred frames may happen sparsely. The proposed work involves a novel motion deblurring algorithm in
which a blurred frame can be reconstructed utilizing the high-resolution information of adjacent unblurred frames. First, a
motion- compensated predictor for the blurred frame is derived from its neighboring unblurred frame via specific motion
estimation. Then, an accurate blur kernel is computed using both predictor and the blurred frame. Next, a residual deconvolution
is applied to both of those frames in order to reduce the ringing artifacts inherently caused by conventional deconvolution. The
blur kernel estimation and deconvolution processes are iteratively performed for the deblurred frame. Simulation results show
that the proposed algorithm provides superior deblurring results over conventional deblurring algorithms while preserving
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