Densely Connected Hierarchical Network for Image Denoising

Park, Bumjun; Yu, Songhyun; Jeong, Jechang

doi:10.1109/cvprw.2019.00263

Cited by 101 publications

(70 citation statements)

References 34 publications

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“…Mode collapse happens when the generator outputs the same image for different inputs. Though other methods [2][3][4][5][6][7][8][9][10][20][21][22][23][24] can also offer image-to-image translation with unpaired images, Cycle GAN has become a common platform for many image translation related tasks.…”

Section: Cycle Ganmentioning

confidence: 99%

“…Image-to-image conversion, such as data augmentation [1] or style transfer [2], has been applied to recent computer vision applications. Traditional image conversion models had been investigated for specific applications [3][4][5][6][7][8][9][10][11][12][13][14]. Since the creation of the GAN model [15], it opened a new door to train generative models for image conversion.…”

Section: Introductionmentioning

confidence: 99%

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Generative Adversarial Networks for Visible to Infrared Video Conversion

Uddin¹,

Li²

2020

Recent Advances in Image Restoration With Applications to Real World Problems

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Deep learning models are data driven. For example, the most popular convolutional neural network (CNN) model used for image classification or object detection requires large labeled databases for training to achieve competitive performances. This requirement is not difficult to be satisfied in the visible domain since there are lots of labeled video and image databases available nowadays. However, given the less popularity of infrared (IR) camera, the availability of labeled infrared videos or image databases is limited. Therefore, training deep learning models in infrared domain is still challenging. In this chapter, we applied the pix2pix generative adversarial network (Pix2Pix GAN) and cycle-consistent GAN (Cycle GAN) models to convert visible videos to infrared videos. The Pix2Pix GAN model requires visible-infrared image pairs for training while the Cycle GAN relaxes this constraint and requires only unpaired images from both domains. We applied the two models to an open-source database where visible and infrared videos provided by the signal multimedia and telecommunications laboratory at the Federal University of Rio de Janeiro. We evaluated conversion results by performance metrics including Inception Score (IS), Frechet Inception Distance (FID) and Kernel Inception Distance (KID). Our experiments suggest that cycle-consistent GAN is more effective than pix2pix GAN for generating IR images from optical images.

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Section: Cycle Ganmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Generative Adversarial Networks for Visible to Infrared Video Conversion

Uddin¹,

Li²

2020

Recent Advances in Image Restoration With Applications to Real World Problems

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“…We used same channel attention architecture with RCAN. Kernel Attention CNN for Image Denoising (KADN) is inspired by Selective Kernel Networks (SKNet) [22], DeepLab V3 [10], and Densely Connected Hierarchical Network for Image Denoising (DHDN) [35]. The DCR blocks of DHDN are replaced with Kernel Attention (KA) blocks.…”

Section: Erasermentioning

confidence: 99%

NTIRE 2019 Challenge on Real Image Denoising: Methods and Results

Abdelhamed

Timofte²,

Brown³

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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This paper reviews the NTIRE 2020 challenge on real image denoising with focus on the newly introduced dataset, the proposed methods and their results. The challenge is a new version of the previous NTIRE 2019 challenge on real image denoising that was based on the SIDD benchmark. This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+. This challenge has two tracks for quantitatively evaluating image denoising performance in (1) the Bayer-pattern rawRGB and (2) the standard RGB (sRGB) color spaces. Each track ∼250 registered participants. A total of 22 teams, proposing 24 methods, competed in the final phase of the challenge. The proposed methods by the participating teams represent the current state-of-the-art performance in image denoising targeting real noisy images. The newly collected SIDD+ datasets are publicly available at: https://bit.ly/siddplus_data. A. Abdelhamed (kamel@eecs.yorku.ca, York University), M. Afifi, R. Timofte, and M.S. Brown are the NTIRE 2020 challenge organizers, while the other authors participated in the challenge. Appendix A contains the authors' teams and affiliations. NTIRE webpage: arXiv:2005.04117v1 [cs.CV] 8 May 2020

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“…The structure of UNet++ is essentially a deeply supervised encoder-decoder network, where the encoder and decoder sub-networks are connected through a series of nested dense cross-layers. Since UNet++ is an encoder-decoder network, it is suitable for image restoration tasks such as image denoising and defogging as well [30]. The U-Net structure has been utilized by Xie [31], Ivana [32], and some others for RGB-NIR image demosaicking tasks.…”

Section: Introductionmentioning

confidence: 99%

“…Since UNet++ is an encoder–decoder network, it is suitable for image restoration tasks such as image denoising and defogging as well [ 30 ]. The U-Net structure has been utilized by Xie [ 31 ], Ivana [ 32 ], and some others for RGB–NIR image demosaicking tasks.…”

Section: Introductionmentioning

confidence: 99%

A Compact High-Quality Image Demosaicking Neural Network for Edge-Computing Devices

Wang

Zhao

Dou

et al. 2021

Sensors

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Image demosaicking has been an essential and challenging problem among the most crucial steps of image processing behind image sensors. Due to the rapid development of intelligent processors based on deep learning, several demosaicking methods based on a convolutional neural network (CNN) have been proposed. However, it is difficult for their networks to run in real-time on edge computing devices with a large number of model parameters. This paper presents a compact demosaicking neural network based on the UNet++ structure. The network inserts densely connected layer blocks and adopts Gaussian smoothing layers instead of down-sampling operations before the backbone network. The densely connected blocks can extract mosaic image features efficiently by utilizing the correlation between feature maps. Furthermore, the block adopts depthwise separable convolutions to reduce the model parameters; the Gaussian smoothing layer can expand the receptive fields without down-sampling image size and discarding image information. The size constraints on the input and output images can also be relaxed, and the quality of demosaicked images is improved. Experiment results show that the proposed network can improve the running speed by 42% compared with the fastest CNN-based method and achieve comparable reconstruction quality as it on four mainstream datasets. Besides, when we carry out the inference processing on the demosaicked images on typical deep CNN networks, Mobilenet v1 and SSD, the accuracy can also achieve 85.83% (top 5) and 75.44% (mAP), which performs comparably to the existing methods. The proposed network has the highest computing efficiency and lowest parameter number through all methods, demonstrating that it is well suitable for applications on modern edge computing devices.

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Densely Connected Hierarchical Network for Image Denoising

Cited by 101 publications

References 34 publications

Generative Adversarial Networks for Visible to Infrared Video Conversion

Generative Adversarial Networks for Visible to Infrared Video Conversion

NTIRE 2019 Challenge on Real Image Denoising: Methods and Results

A Compact High-Quality Image Demosaicking Neural Network for Edge-Computing Devices

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