This paper presents a new bandwidth adaptive nonlocal kernel regression (BA-NLKR) algorithm for image and video restoration. NLKR is a recent approach for improving the performance of conventional steering kernel regression (SKR) and local polynomial regression (LPR) in image/video processing. Its bandwidth, which controls the amount of smoothing, however is chosen empirically. The proposed algorithm incorporates the intersecting confidence intervals (ICI) bandwidth selection method into the framework of NLKR to facilitate automatic bandwidth selection so as to achieve better performance. A parallel implementation of the proposed algorithm is also introduced to reduce significantly its computation time. The effectiveness of the proposed algorithm is illustrated by experimental results on both single image and videos super resolution and denoising.
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