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Found 7 papers, 5 papers with code
Neural Degradation Representation Learning for All-In-One Image Restoration
To this end, we propose to learn a neural degradation representation (NDR) that captures the underlying characteristics of various degradations.
Generalized Lightness Adaptation with Channel Selective Normalization
Existing methods typically work well on their trained lightness conditions but perform poorly in unknown ones due to their limited generalization ability.
Mutual-Guided Dynamic Network for Image Fusion
Image fusion aims to generate a high-quality image from multiple images captured under varying conditions.
Zero-Shot Dual-Lens Super-Resolution
To overcome these two challenges, we propose a degradation-invariant alignment method and a degradation-aware training strategy to fully exploit the information within a single dual-lens pair.
Toward RAW Object Detection: A New Benchmark and a New Model
In many computer vision applications (e. g., robotics and autonomous driving), high dynamic range (HDR) data is necessary for object detection algorithms to handle a variety of lighting conditions, such as strong glare.
Continuous Spectral Reconstruction from RGB Images via Implicit Neural Representation
In this paper, we propose Neural Spectral Reconstruction (NeSR) to lift this limitation, by introducing a novel continuous spectral representation.
EDPN: Enhanced Deep Pyramid Network for Blurry Image Restoration
Image deblurring has seen a great improvement with the development of deep neural networks.
