Search Results for author:
Found 7 papers, 5 papers with code
Comprehensive and Delicate: An Efficient Transformer for Image Restoration
Vision Transformers have shown promising performance in image restoration, which usually conduct window- or channel-based attention to avoid intensive computations.
Relationship Quantification of Image Degradations
To tackle the first challenge, we proposed a Degradation Relationship Index (DRI) which is defined as the mean drop rate difference in the validation loss between two models which are respectively trained using the anchor degradation and the mixture of the anchor and the auxiliary degradations.
All-in-One Image Restoration for Unknown Corruption
In this paper, we study a challenging problem in image restoration, namely, how to develop an all-in-one method that could recover images from a variety of unknown corruption types and levels.
Unsupervised Neural Rendering for Image Hazing
To generate plausible haze, we study two less-touched but challenging problems in hazy image rendering, namely, i) how to estimate the transmission map from a single image without auxiliary information, and ii) how to adaptively learn the airlight from exemplars, i. e., unpaired real hazy images.
COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction
In this paper, we study two challenging problems in incomplete multi-view clustering analysis, namely, i) how to learn an informative and consistent representation among different views without the help of labels and ii) how to recover the missing views from data.
Ranked #1 on
Incomplete multi-view clustering
on n-MNIST
CLEARER: Multi-Scale Neural Architecture Search for Image Restoration
Different from the existing labor-intensive handcrafted architecture design paradigms, we present a novel method, termed as multi-sCaLe nEural ARchitecture sEarch for image Restoration (CLEARER), which is a specifically designed neural architecture search (NAS) for image restoration.
You Only Look Yourself: Unsupervised and Untrained Single Image Dehazing Neural Network
In this paper, we study two challenging and less-touched problems in single image dehazing, namely, how to make deep learning achieve image dehazing without training on the ground-truth clean image (unsupervised) and a image collection (untrained).
