2 February 2012 Super-resolution image reconstruction with edge adaptive weight in video sequence
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Abstract
Digital images and videos are used in many digital devices recently. Also, the resolution of display became larger than that of previous years. Image up-scaling algorithm is important issue since original input source is limited in transferring within data bandwidth. Among various up-scaling algorithms, Super-Resolution (SR) image reconstruction method is able to estimate high-resolution (HR) image using multiple low-resolution (LR) images. Conventional approaches to estimate HR image with Least Square (LS) method and Weighted Least Square (WLS) method are not able to reconstruct high-frequency region effectively in case its blur kernel is assumed Gaussian kernel in unknown system. Also, these methods produce jagging artifacts from the deficiency of LR frames. The proposed SR algorithm uses edge adaptive WLS method to reconstruct high-frequency region considering local properties and is applied to video sequence with block process to cope with local motions. Moreover, to apply video sequence with complex motions, we use selectively the correct information of reference frame to avoid errors from incorrect information. For accurate additional information from reference frames, the proposed algorithm determines additional information in reference frame by comparing with current frame and reference frame. The experiments demonstrate the superior performance of the proposed algorithm.
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Ji Yong Kwon, Du Sic Yoo, Jong Hyun Park, Se Hyeok Park, Moon Gi Kang, "Super-resolution image reconstruction with edge adaptive weight in video sequence", Proc. SPIE 8295, Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II, 82950M (2 February 2012); doi: 10.1117/12.905368; https://doi.org/10.1117/12.905368
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