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Found 7 papers, 6 papers with code
GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detection
Therefore, instead of utilizing the same setting for all samples, we propose to predict a particular denoising step for each sample by evaluating the difference between image contents and the priors extracted from diffusion models.
Ranked #2 on
Anomaly Detection
on MPDD
Exposure Bracketing is All You Need for Unifying Image Restoration and Enhancement Tasks
It is highly desired but challenging to acquire high-quality photos with clear content in low-light environments.
Self-Supervised Image Restoration with Blurry and Noisy Pairs
By learning in a collaborative manner, the deblurring and denoising tasks in our method can benefit each other.
Incorporating Semi-Supervised and Positive-Unlabeled Learning for Boosting Full Reference Image Quality Assessment
Particularly, by treating all labeled data as positive samples, PU learning is leveraged to identify negative samples (i. e., outliers) from unlabeled data.
Adaptive Network Combination for Single-Image Reflection Removal: A Domain Generalization Perspective
Meanwhile, diverse testing sets are also provided with different types of reflection and scenes.
Flexible Image Denoising with Multi-layer Conditional Feature Modulation
For flexible non-blind image denoising, existing deep networks usually take both noisy image and noise level map as the input to handle various noise levels with a single model.
Toward Convolutional Blind Denoising of Real Photographs
While deep convolutional neural networks (CNNs) have achieved impressive success in image denoising with additive white Gaussian noise (AWGN), their performance remains limited on real-world noisy photographs.
Ranked #4 on
Denoising
on Darmstadt Noise Dataset
