Wenxuan Shi

TL;DR: This work equips the detection framework with another new strategy, and extract the new features, to eliminate the above requirements, and formulates SWA and pHash into a joint descriptor, called HASP, to improve the detection performance significantly.

TL;DR: In these comprehensive experiments, the nonlocal sparse regularization-based image deblurring with novel similarity criteria called structural similarity distance and principal component analysis-subspace Euclidean distance has achieved higher peak signal-to-noise and favorable consistency with subjective vision perception compared with state-of-the-artdeblurring algorithms.

TL;DR: This paper introduces an efficient feature learning framework via sparse coding for no-reference image quality assessment through sparse feature extraction from a sparse representation matrix, which is computed using a sparse coding algorithm.

TL;DR: This paper presents an approach for the reconstruction of the color contact image sensor that combines the sparse prior that often used in super-resolution and the inter-channel correlation prior that the majority of image demosaicing algorithms used to solve this problem.

TL;DR: The Table 5 was updated and the footnote of the Table 5, ‘The 'n/a' entries in the table means that bounding box or part annotation is not used’ was incorrectly given as ‘ the results are not available.’