AIM 2019 Challenge on Image Demoireing: Methods and Results

Yuan, Shanxin; Fu, Zhenyong; Yang, Jian; Hong, Minghui; Lin, Wenying; Yang, Wenzhu; Qu, Yanyun; Shin, Hong-Kyu; Joon-Yeon, Kim,; Ko, Seung Hyeon; Dong, Hang; Timofte, Radu; Yu, Guangchuang; Wang, Jie; Ding, Xuan; Han, Zongyan; Das, Sourya Dipta; Purohit, Kuldeep; Kandula, Praveen; Suin, Maitreya; Rajagopalan, A. N.; Slabaugh, Greg; Leonardis, Aleš; Zheng, Bolun; Ye, Xin; Tian, Xiang; Chen, Yaowu; Cheng, Xi

doi:10.1109/iccvw.2019.00438

Cited by 25 publications

(14 citation statements)

References 29 publications

(40 reference statements)

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“…The RGB to spectra recovery challenge [9] is one of the NTIRE 2020 challenges. The other challenges are: deblurring [40], nonhomogeneous dehazing [5], perceptual extreme super-resolution [63], video quality mapping [18], real image denoising [1], real-world super-resolution [35] and demoireing [60].…”

Section: Ntire 2020 Challengementioning

confidence: 99%

NTIRE 2018 Challenge on Spectral Reconstruction from RGB Images

Arad

Liu

et al. 2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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124

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This paper reviews the second challenge on spectral reconstruction from RGB images, i.e., the recovery of wholescene hyperspectral (HS) information from a 3-channel RGB image. As in the previous challenge, two tracks were provided: (i) a "Clean" track where HS images are estimated from noise-free RGBs, the RGB images are themselves calculated numerically using the ground-truth HS images and supplied spectral sensitivity functions (ii) a "Real World" track, simulating capture by an uncalibrated and unknown camera, where the HS images are recovered from noisy JPEG-compressed RGB images. A new, larger-than-ever, natural hyperspectral image data set is presented, containing a total of 510 HS images. The Clean and Real World tracks had 103 and 78 registered participants respectively, with 14 teams competing in the final testing phase. A description of the proposed methods, alongside their challenge scores and an extensive evaluation of top performing methods is also provided. They gauge the state-of-the-art in spectral reconstruction from an RGB image. arXiv:2005.03412v1 [eess.IV] 7 May 2020

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Section: Ntire 2020 Challengementioning

confidence: 99%

NTIRE 2018 Challenge on Spectral Reconstruction from RGB Images

Arad

Liu

et al. 2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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124

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“…The VAADER team's method is inspired by MBCNN of the AIM2019 challenge [59] and uses a CNN-based multiscale approach. A multi loss extracted from the flow at different scales is used to train the network.…”

Section: Vaader Teammentioning

confidence: 99%

NTIRE 2020 Challenge on Image Demoireing: Methods and Results

Yuan

Timofte²,

Leonardis³

et al. 2020

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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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. Demoireing is a difcult task of removing moire patterns from an image to reveal an underlying clean image. The challenge was divided into two tracks. Track 1 targeted the single image demoireing problem, which seeks to remove moire patterns from a single image. Track 2 focused on the burst demoireing problem, where a set of degraded moire images of the same scene were provided as input, with the goal of producing a single demoired image as output. The methods were ranked in terms of their fidelity, measured using the peak signal-to-noise ratio (PSNR) between the ground truth clean images and the restored images produced by the participants' methods. The tracks had 142 and 99 registered participants, respectively, with a total of 14 and 6 submissions in the final testing stage. The entries span the current state-of-the-art in image and burst image demoireing problems.

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“…We first compare with the participating methods in the AIM19 image demoireing challenge [42]. The results on the validation set (again, independent and not used in training) is shown in Table 5.…”

Section: Comparison On Lcdmoire Datasetmentioning

confidence: 99%

Image Demoireing with Learnable Bandpass Filters

Zheng

Yuan

Slabaugh

et al. 2020

Preprint

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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, and 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-ofthe-art methods by a large margin (more than 2dB in terms of PSNR).

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AIM 2019 Challenge on Image Demoireing: Methods and Results

Cited by 25 publications

References 29 publications

NTIRE 2018 Challenge on Spectral Reconstruction from RGB Images

NTIRE 2018 Challenge on Spectral Reconstruction from RGB Images

NTIRE 2020 Challenge on Image Demoireing: Methods and Results

Image Demoireing with Learnable Bandpass Filters

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