31 citations · 62 across the 5 of their papers we have counts for
5 papers
Make Explicit Calibration Implicit: Calibrate Denoiser Instead of the Noise Model
Xin Jin, Jia-Wen Xiao, Ling-Hao Han +4
Explicit calibration-based methods have dominated RAW image denoising under extremely low-light environments. However, these methods are impeded by several critical limitations: a)…
AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation
Zhen Li, Zuo-Liang Zhu, Ling-Hao Han +3
We present All-Pairs Multi-Field Transforms (AMT), a new network architecture for video frame interpolation. It is based on two essential designs. First, we build bidirectional cor…
Underwater Ranker: Learn Which Is Better and How to Be Better
Chunle Guo, Ruiqi Wu, Xin Jin +4
In this paper, we present a ranking-based underwater image quality assessment (UIQA) method, abbreviated as URanker. The URanker is built on the efficient conv-attentional image Tr…
Deeply Explain CNN via Hierarchical Decomposition
Ming-Ming Cheng, Peng-Tao Jiang, Ling-Hao Han +2
In computer vision, some attribution methods for explaining CNNs attempt to study how the intermediate features affect the network prediction. However, they usually ignore the feat…
Low-Light Image and Video Enhancement Using Deep Learning: A Survey
Chongyi Li, Chunle Guo, Linghao Han +4
Low-light image enhancement (LLIE) aims at improving the perception or interpretability of an image captured in an environment with poor illumination. Recent advances in this area…