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The image super-resolution (SR) technique, which aims at reconstructing a high-resolution (HR) image from a single low-resolution (LR) image, is a classical problem in computer vision. Limited by the imaging hardware, the spatial resolution of a hyperspectral images (HSI) is usually very coarse. Meanwhile, the spectral information of the HSI is extremely important for its applications and cannot be...
Single image super-resolution (SR) generates a high-resolution (HR) image by estimating the mapping function between image patches of different resolutions. By leveraging the notion of regression, the mapping function estimation task is often transformed into predicting mapping function's derivatives. Although higher-orders of derivative lead to a more accurate mapping function, current algorithms...
CARS holography combines the chemical selectivity of CARS with the amplitude and phase imaging capability of holography to establish a novel label-free imaging technique. Here, we review CARS holography and its application to biological imaging.
Super-resolution (SR) image reconstruction is a rapidly developing area in image processing. Especially, blind SR can generate high space resolution image without requiring priori information of the point spread function (PSF). In this paper, we propose a self-adaptive blind super-resolution image reconstruction approach which is based on multiple images. Our method can adaptively choose the parameter...
We generate nonuniform quantization parameter (QP) matrices to improve the perceptual quality of reconstructed video in the AVC/H.264 standard. The resulting 9 QP matrices are indexed to the 9 intra-frame prediction modes of the 4 times 4 blocks and can be applied to the 16 times 16 macroblock (MB)s which engage only the first 4 out of the 9 intra-modes. We also studied how the nonuniform QP matrices...
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