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In example-based super-resolution, it is difficult to determine appropriate high-frequency (HF) patches from a training database by using only the information of one input image. In this letter, we utilize the sharpness of high-resolution (HR) patch candidates for the reliable determination of HF patches. For each input patch, we first preselect a sufficient number of HF patch candidates and produce HR patches by adding the candidates to the input patch. After removing the outlier patches, we then reselect several HF patches according to the patch characteristic for producing the final HR image. This reselection procedure is optimized for edge patches and non-edge patches, respectively. Experimental results show that the proposed algorithm provides sharper details compared to the existing algorithms.

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Image retention is a defect related with all types of displays, but testing for it has not been redefined since the early years of TV. As the era of HDR approaches, new test procedures for determining the degree of image retention in modern consumer display products needs to be undertaken.

No abstract