# Blind Image Deblurring   Edit

8 papers with code • 0 benchmarks • 0 datasets

Blind Image Deblurring is a classical problem in image processing and computer vision, which aims to recover a latent image from a blurred input.

# Learning Deep Gradient Descent Optimization for Image Deconvolution

10 Apr 2018

Extensive experiments on synthetic benchmarks and challenging real-world images demonstrate that the proposed deep optimization method is effective and robust to produce favorable results as well as practical for real-world image deblurring applications.

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# 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.

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# Spatially-Adaptive Filter Units for Compact and Efficient Deep Neural Networks

20 Feb 2019

Convolutional neural networks excel in a number of computer vision tasks.

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# Raw Image Deblurring

8 Dec 2020

Therefore, we built a new dataset containing both RAW images and processed sRGB images and design a new model to utilize the unique characteristics of RAW images.

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# Explore Image Deblurring via Blur Kernel Space

1 Apr 2021

This paper introduces a method to encode the blur operators of an arbitrary dataset of sharp-blur image pairs into a blur kernel space.

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# Efficient Blind Deblurring under High Noise Levels

19 Apr 2019

In this work, we first show that current state-of-the-art kernel estimation methods based on the $\ell_0$ gradient prior can be adapted to handle high noise levels while keeping their efficiency.

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# Deblurring using Analysis-Synthesis Networks Pair

Unlike existing deblurring networks, this design allows us to explicitly incorporate the blur-kernel in the network's training.

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# Deep Wiener Deconvolution: Wiener Meets Deep Learning for Image Deblurring

We present a simple and effective approach for non-blind image deblurring, combining classical techniques and deep learning.

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