Yanling Li

TL;DR: Experimental results on natural images show that the proposed adaptive CS scheme improves the visual quality of reconstructed image, and has better rate-distortion performance than the existing adaptive CS schemes.

TL;DR: Experimental results show that the proposed scheme not only has a low encoding and decoding complexity when compared with traditional methods, but it also provides good objective and subjective reconstruction qualities, and presents better time-distortion performance than JPEG.

TL;DR: Using patch-based sparseland model, a novel Motion-Compensated Frame Interpolation (MCFI) method is designed that outperforms the existing algorithms in both objective and subjective picture qualities, but it introduces a high computational complexity in the meantime.

TL;DR: The Granular Computing GrC is used to decompose an image into several granules depending on the structural features of patches and the PCA is performed to learn the sparse representation basis corresponding to each granule, which can effectively improve the performance of hard-thresholding shrinkage.