computer-vision-11

Image Deblurring

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in image-deblurring-11

Trend

Dataset

Best Model

Actions

GoPro

HIDE

ImageNet

Libraries

i

Use these libraries to find image-deblurring-11 models and implementations

swz30/MPRNet

2 papers 1,097

Datasets

Leishmania parasite dataset

Subtasks

Low-light Image Deblurring and Enhancement

Most implemented papers

Simple Baselines for Image Restoration

X. Zhang, Liangyu Chen, Xiaojie Chu, Jian Sun•Sat Apr 09 2022

A simple baseline is proposed that exceeds the SOTA methods and is computationally efficient and reveals that the nonlinear activation functions, e.g. Sigmoid, ReLU, GELU, Softmax, etc. are not necessary: they could be replaced by multiplication or removed.

1273

Content

CelebA

HIDE (trained on GOPRO)

RealBlur-R(trained on GoPro)

2 papers 626

2 papers 468

2 papers 368

2 papers 141

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Paper Graph

Restormer: Efficient Transformer for High-Resolution Image Restoration

Salman Hameed Khan, Munawar Hayat, Ming-Hsuan Yang, Syed Waqas Zamir, F. Khan, Aditya Arora•Wed Nov 17 2021

This work proposes an efficient Transformer model by making several key designs in the building blocks (multi-head attention and feed-forward network) such that it can capture long-range pixel interactions, while still remaining applicable to large images.

3334 0

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DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better

Zhangyang Wang, T. Martyniuk, Orest Kupyn, Junru Wu•Fri Aug 09 2019

It is demonstrated that DeblurGAN-V2 has very competitive performance on several popular benchmarks, in terms of deblurring quality (both objective and subjective), as well as efficiency, and is effective for general image restoration tasks too.

1050 0

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Multi-Stage Progressive Image Restoration

Ling Shao, Salman Hameed Khan, Munawar Hayat, Syed Waqas Zamir, F. Khan, Ming-Hsuan Yang, Aditya Arora•Wed Feb 03 2021

This paper proposes a novel synergistic design that can optimally balance these competing goals in image restoration tasks, that progressively learns restoration functions for the degraded inputs, thereby breaking down the overall recovery process into more manageable steps.

1950 0

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Scale-Recurrent Network for Deep Image Deblurring

Xiaoyong Shen, Jiaya Jia, Jue Wang, Xin Tao, Hongyun Gao, Yi Wang•Mon Feb 05 2018

A Scale-recurrent Network (SRN-DeblurNet) is proposed and shown to produce better quality results than state-of-the-arts, both quantitatively and qualitatively in single image deblurring.

1224 0

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Uformer: A General U-Shaped Transformer for Image Restoration

Jianmin Bao, Xiaodong Cun, Jianzhuang Liu, Zhendong Wang•Sat Jun 05 2021

Uformer, an effective and efficient Transformer-based architecture for image restoration, in which a hierarchical encoder-decoder network is built using the Transformer block and a learnable multi-scale restoration modulator in the form of a multi- scale spatial bias to adjust features in multiple layers of the Uformer decoder is proposed.

1921 0

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Rethinking Coarse-to-Fine Approach in Single Image Deblurring

Seung‐Won Jung, Sung-Jin Cho, Seoyoun Ji, Jun-Pyo Hong, Sung-Jea Ko•Tue Aug 10 2021

This work revisits the coarse-to-fine strategy and presents a multi-input multi-output U-net (MIMO-UNet), which outperforms the state-of-the-art methods in terms of both accuracy and computational complexity.

730 0

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Residual Dense Network for Image Restoration

Yapeng Tian, Yu Kong, Y. Fu, Yulun Zhang, Bineng Zhong•Mon Dec 24 2018

This work proposes residual dense block (RDB) to extract abundant local features via densely connected convolutional layers and proposes local feature fusion in RDB to adaptively learn more effective features from preceding and current local features and stabilize the training of wider network.

819 0

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Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model

Yinhuai Wang, Jiwen Yu, Jian Zhang•Wed Nov 30 2022

This work proposes the Denoising Diffusion Null-Space Model (DDNM), a novel zero-shot framework for arbitrary linear IR problems, including but not limited to image super-resolution, colorization, inpainting, compressed sensing, and deblurring, and proposes an enhanced and robust version, dubbed DDNM+, to support noisy restoration and improve restoration quality for hard tasks.

625 0

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The Little Engine That Could: Regularization by Denoising (RED)

P. Milanfar, Michael Elad, Yaniv Romano•Tue Nov 08 2016

This paper provides an alternative, more powerful, and more flexible framework for achieving Regularization by Denoising (RED): using the denoising engine in defining the regulariza...

907 0

Paper Graph

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