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Found 5 papers, 2 papers with code
Few-Shot Domain Adaptation for Low Light RAW Image Enhancement
Enhancing practical low light raw images is a difficult task due to severe noise and color distortions from short exposure time and limited illumination.
Ranked #1 on
Low-Light Image Enhancement
on Canon RAW Low Light
Segmentation Guided Deep HDR Deghosting
Our motion segmentation guided HDR fusion approach offers significant advantages over existing HDR deghosting methods.
Self-Gated Memory Recurrent Network for Efficient Scalable HDR Deghosting
In the SGM cell, the information flow through a gate is controlled by multiplying the gate's output by a function of itself.
Labeled From Unlabeled: Exploiting Unlabeled Data for Few-Shot Deep HDR Deghosting
To derive data for the next stage of training, we propose a novel method for generating corresponding dynamic inputs from the predicted HDRs of unlabeled data.
DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs
To address the above issues, we have gathered a large dataset of multi-exposure image stacks for training and to circumvent the need for ground truth images, we propose an unsupervised deep learning framework for MEF utilizing a no-reference quality metric as loss function.
