Image Demoireing with Learnable Bandpass Filters
Bolun Zheng,Shanxin Yuan,Gregory G. Slabaugh,Ales Leonardis +3 more
- pp 3636-3645
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TLDR
In this paper, a multiscale bandpass convolutional neural network (MBCNN) was proposed to solve both texture and color restoration problems in an end-to-end manner.Abstract:
Image demoireing is a multi-faceted image restoration task involving both texture and color restoration. In this paper, we propose a novel multiscale bandpass convolutional neural network (MBCNN) to address this problem. As an end-to-end solution, MBCNN respectively solves the two sub-problems. For texture restoration, we propose a learnable bandpass filter (LBF) to learn the frequency prior for moire texture removal. For color restoration, we propose a two-step tone mapping strategy, which first applies a global tone mapping to correct for a global color shift, then performs local fine tuning of the color per pixel. Through an ablation study, we demonstrate the effectiveness of the different components of MBCNN. Experimental results on two public datasets show that our method outperforms state-of-the-art methods by a large margin (more than 2dB in terms of PSNR).read more
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Posted Content
NTIRE 2020 Challenge on Image Demoireing: Methods and Results
Shanxin Yuan,Radu Timofte,Ales Leonardis,Gregory G. Slabaugh,Xiaotong Luo,Jiangtao Zhang,Yanyun Qu,Ming Hong,Yuan Xie,Cuihua Li,Dejia Xu,Yihao Chu,Qingyan Sun,Shuai Liu,Ziyao Zong,Nan Nan,Chenghua Li,Sangmin Kim,Hyungjoon Nam,Jisu Kim,Jechang Jeong,Manri Cheon,Sung-Jun Yoon,Byungyeon Kang,Junwoo Lee,Bolun Zheng,Xiaohong Liu,Linhui Dai,Jun Chen,Xi Cheng,Zhenyong Fu,Jian Yang,Chul Lee,An Gia Vien,Hyunkook Park,Sabari Nathan,M. Parisa Beham,S. Mohamed Mansoor Roomi,Florian Lemarchand,Maxime Pelcat,Erwan Nogues,Densen Puthussery,Hrishikesh P S,C. V. Jiji,Ashish Sinha,Xuan Zhao +45 more
TL;DR: This paper reviews the Challenge on Image Demoireing that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2020, and examines the current state-of-the-art in image and burst image demoireing problems.
Book ChapterDOI
Wavelet-Based Dual-Branch Network for Image Demoiréing
Lin Liu,Lin Liu,Jianzhuang Liu,Shanxin Yuan,Gregory G. Slabaugh,Ales Leonardis,Wengang Zhou,Qi Tian +7 more
TL;DR: Although designed for image demoireing, WDNet has been applied to two other low-levelvision tasks, outperforming state-of-the-art image deraining and derain-drop methods on the Rain100h and Raindrop800 data sets, respectively.
Journal ArticleDOI
Dynamic coarse-to-fine ISAR image blind denoising using active joint prior learning
TL;DR: Extensive experimental results on ISAR image datasets demonstrate the effectiveness of the proposed model for both synthesis and real‐world noisy ISAR images, and the proposed method outperforms the state‐of‐the‐art denoising methods.
Posted Content
Real-time Image Enhancer via Learnable Spatial-aware 3D Lookup Tables
TL;DR: Zhang et al. as mentioned in this paper proposed a real-time image enhancement via learnable spatial-aware 3-dimentional lookup tables (3D LUTs), which well considers global scenario and local spatial information.
Journal ArticleDOI
Learning Frequency Domain Priors for Image Demoireing
TL;DR: Wang et al. as mentioned in this paper proposed a multi-block-size learnable bandpass filters (M-LBFs), based on a block-wise frequency domain transform, to learn the frequency domain priors of moire patterns.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Book ChapterDOI
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Journal ArticleDOI
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky,Jia Deng,Hao Su,Jonathan Krause,Sanjeev Satheesh,Sean Ma,Zhiheng Huang,Andrej Karpathy,Aditya Khosla,Michael S. Bernstein,Alexander C. Berg,Li Fei-Fei +11 more
TL;DR: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) as mentioned in this paper is a benchmark in object category classification and detection on hundreds of object categories and millions of images, which has been run annually from 2010 to present, attracting participation from more than fifty institutions.
Proceedings ArticleDOI
Fully convolutional networks for semantic segmentation
TL;DR: The key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning.