Abstract
Research on high-performance signal processing for high-resolution image sensor to be used in mobile communication, defense, medical, and automobile fields is actively being conducted all over the world. Various methods such as image interpolation and super resolution method are used as a method for restoring deterioration of image quality after various conversion of an image. Using more information than necessary to obtain the target image quality increases computational complexity, resulting in unnecessary computation and power consumption. In this paper, we propose and analyze a fast method for low-rank matrix optimization for the restoration of low resolution images that are severely exposed to noise for signals with non-local self-similarity characteristics. It is expected that this result can be used in fields requiring the highest signal quality of multi-sensor fusion for given visual applications such as smart devices and automobiles.
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