Hongwei Yong

TL;DR: This work establishes a large-scale database named the Waterloo Exploration Database, which in its current state contains 4744 pristine natural images and 94 880 distorted images created from them, and presents three alternative test criteria to evaluate the performance of IQA models, namely, the pristine/distorted image discriminability test, the listwise ranking consistency test, and the pairwise preference consistency test.

TL;DR: Li et al. as mentioned in this paper proposed a Laplacian pyramid based kernel prediction network (LP-KPN), which efficiently learns per-pixel kernels to recover the HR image, which achieved better visual quality with sharper edges and finer textures on real-world scenes.

TL;DR: The proposed algorithm not only outperforms previous MEF algorithms on static scenes but also consistently produces high quality fused images with little ghosting artifacts for dynamic scenes and maintains a lower computational cost compared with the state-of-the-art deghosting schemes.

TL;DR: Wang et al. as mentioned in this paper proposed an effective online background subtraction method, which can be robustly applied to real-time videos that have variations in both foreground and background.

TL;DR: This work proposes a new variational inference method, which integrates both noise estimation and image denoising into a unique Bayesian framework, for blind image Denoising, and presents an approximate posterior, parameterized by deep neural networks, presented by taking the intrinsic clean image and noise variances as latent variables conditioned on the input noisy image.