Yu Zhu

TL;DR: This work proposes a self-adaptive hyper network architecture to blind assess image quality in the wild, which outperforms the state-of-the-art methods on challenging authentic image databases but also achieves competing performances on synthetic image databases, though it is not explicitly designed for the synthetic task.

TL;DR: Experiments evaluate the deformation effectiveness and super-resolution performance, showing that the deformable patches help improve the representation accuracy and perform better than the state-of-art methods.

TL;DR: This study presents a novel deep framework, termed NHDRRnet, which adopts an alternative direction and attempts to remove ghosting artifacts by exploiting the non-local correlation in inputs and incorporates a triple-pass residual module to capture more powerful local features, which proves to be effective in further boosting the performance.

TL;DR: The AIM 2020 challenge on efficient single image super-resolution was to super-resolve an input image with a magnification factor x4 based on a set of prior examples of low and corresponding high resolution images with focus on the proposed solutions and results.

TL;DR: A ghost-free HDR image synthesis algorithm that utilizes a sparse representation framework based on the dependency among adjacent low dynamic range images and the sparsity of the moving object that leads to the ghost artifacts is proposed.