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Learning to Sample the Most Useful Training Patches from Images
Shuyang Sun, Liang Chen, Gregory Slabaugh +1
Some image restoration tasks like demosaicing require difficult training samples to learn effective models. Existing methods attempt to address this data training problem by manual…
Self-Adaptively Learning to Demoire from Focused and Defocused Image Pairs
Lin Liu, Shanxin Yuan, Jianzhuang Liu +3
Moire artifacts are common in digital photography, resulting from the interference between high-frequency scene content and the color filter array of the camera. Existing deep lear…
Diagnosing and Preventing Instabilities in Recurrent Video Processing
Thomas Tanay, Aivar Sootla, Matteo Maggioni +4
Recurrent models are a popular choice for video enhancement tasks such as video denoising or super-resolution. In this work, we focus on their stability as dynamical systems and sh…
Video Super-resolution with Temporal Group Attention
Takashi Isobe, Songjiang Li, Xu Jia +6
Video super-resolution, which aims at producing a high-resolution video from its corresponding low-resolution version, has recently drawn increasing attention. In this work, we pro…
Low Light Video Enhancement using Synthetic Data Produced with an Intermediate Domain Mapping
Danai Triantafyllidou, Sean Moran, Steven McDonagh +2
Advances in low-light video RAW-to-RGB translation are opening up the possibility of fast low-light imaging on commodity devices (e.g. smartphone cameras) without the need for a tr…
Wavelet-Based Dual-Branch Network for Image Demoireing
Lin Liu, Jianzhuang Liu, Shanxin Yuan +4
When smartphone cameras are used to take photos of digital screens, usually moire patterns result, severely degrading photo quality. In this paper, we design a wavelet-based dual-b…