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Found 8 papers, 2 papers with code
Structured and Localized Image Restoration
We present a novel approach to image restoration that leverages ideas from localized structured prediction and non-linear multi-task learning.
End-to-end Interpretable Learning of Non-blind Image Deblurring
Non-blind image deblurring is typically formulated as a linear least-squares problem regularized by natural priors on the corresponding sharp picture's gradients, which can be solved, for example, using a half-quadratic splitting method with Richardson fixed-point iterations for its least-squares updates and a proximal operator for the auxiliary variable updates.
Learning to Jointly Deblur, Demosaick and Denoise Raw Images
We address the problem of non-blind deblurring and demosaicking of noisy raw images.
High Dynamic Range and Super-Resolution from Raw Image Bursts
Photographs captured by smartphones and mid-range cameras have limited spatial resolution and dynamic range, with noisy response in underexposed regions and color artefacts in saturated areas.
Fast Two-step Blind Optical Aberration Correction
The optics of any camera degrades the sharpness of photographs, which is a key visual quality criterion.
Handheld Burst Super-Resolution Meets Multi-Exposure Satellite Imagery
Image resolution is an important criterion for many applications based on satellite imagery.
SING: A Plug-and-Play DNN Learning Technique
We propose SING (StabIlized and Normalized Gradient), a plug-and-play technique that improves the stability and generalization of the Adam(W) optimizer.
Collaborative Blind Image Deblurring
Blurry images usually exhibit similar blur at various locations across the image domain, a property barely captured in nowadays blind deblurring neural networks.
