This paper proposes a single image super resolution algorithm with the aim of satisfying three desirable characteristics, namely, high quality of the produced images, adaptability to image contents and unknown blurring conditions used to generate given input images, and low computational complexity. After the given input image is up-scaled using a conventional reconstruction operator, the missing high frequency components estimated from lower resolution versions of the input image are added for improved quality and, moreover, the amount of the high frequency components to be added is adaptively determined. No computationally intensive operation is involved in the whole process, which makes the method computationally cheap. Experimental results show that the proposed method yields good subjective and objective image quality consistently across different blurring conditions and contents, and operates fast in comparison to existing state-of-the-art algorithms. In addition, it is also demonstrated that the proposed method can be used in combination with the existing algorithms in order to improve further their performance in terms of image quality.
Single image super resolution Adaptive weighting Image quality Low computational complexity
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