Improved super-resolution reconstruction algorithm of non-local mean video

Abstract

In view of problems of video super-resolution reconstruction method based on non-local mean (NLM) that reconstructed image was too smooth, convergence speed was slow and calculation amount was large, an improved super-resolution reconstruction algorithm of non-local mean video was proposed. The method uses fuzzy edge complement algorithm to divide preprocessed video image into flat region and texture region; for flat region, image enhancement processing is performed by histogram equalization to reduce the amount of algorithm calculation; for texture region, it is processed by the improved NLM reconstruction algorithm, and similarity weights is corrected by designing a multi-directional adaptive search window and introducing neighborhood coherence coefficients, so as to enhance texture details of the reconstructed image and speed up the convergence of the algorithm; Superimposed normalization of the reconstructed texture region and the enhanced flat region is performed to complete super-resolution reconstruction of the entire video image. The experimental results show that the proposed algorithm can reduce overall complexity of the algorithm and shorten reconstruction time while improving the texture details and peak signal-to-noise ratio of the reconstructed image.

Keywords

• underground video image
• super-resolution reconstruction
• non-local mean
• fuzzy edge complement
• multi-directional adaptive search window
• similarity weight