Gated Fusion Network for Degraded Image Super Resolution

Zhang, Xinyi; Dong, Hang; Hu, Zhe; Lai, Wei Sheng; Wang, Fei; Yang, Ming–Hsuan

doi:10.1007/s11263-019-01285-y

Cited by 57 publications

(58 citation statements)

References 81 publications

(242 reference statements)

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“…Real-world atural images commonly go through multiple kinds of degradations (noise, blur, compression, etc.) at once, and a few recent works were devoted to such join enhancement tasks [32,55] We study the effect of DeblurGAN-v2 on the task of general image restoration. While NOT being the main focus of this paper, we intend to show the general architecture superiority of DeblurGAN-v2, especially for modifications made w.r.t.…”

Section: Extension To General Restorationmentioning

confidence: 99%

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

Kupyn

Martyniuk

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

871

654

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We present a new end-to-end generative adversarial network (GAN) for single image motion deblurring, named DeblurGAN-v2, which considerably boosts state-of-the-art deblurring efficiency, quality, and flexibility. DeblurGAN-v2 is based on a relativistic conditional GAN with a doublescale discriminator. For the first time, we introduce the Feature Pyramid Network into deblurring, as a core building block in the generator of DeblurGAN-v2. It can flexibly work with a wide range of backbones, to navigate the balance between performance and efficiency. The plugin of sophisticated backbones (e.g., Inception-ResNet-v2) can lead to solid state-of-the-art deblurring. Meanwhile, with light-weight backbones (e.g., MobileNet and its variants), DeblurGAN-v2 reaches 10-100 times faster than the nearest competitors, while maintaining close to state-ofthe-art results, implying the option of real-time video deblurring. We demonstrate that DeblurGAN-v2 obtains very competitive performance on several popular benchmarks, in terms of deblurring quality (both objective and subjective), as well as efficiency. Besides, we show the architecture to be effective for general image restoration tasks too. Our codes, models and data are available at: https: //github.com/KupynOrest/DeblurGANv2.

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Section: Extension To General Restorationmentioning

confidence: 99%

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

Kupyn

Martyniuk

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

871

654

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“…Second, they are generally designed for Gaussian-like blur kernel and thus cannot effectively handle severely blurred LR image. It should be noted that a deep blind SISR method for motion blur is proposed in [66]. However, it has limited ability to handle the distortion of arbitrary blur kernels.…”

Section: Related Work 21 Dnn-based Sisrmentioning

confidence: 99%

Deep Plug-And-Play Super-Resolution for Arbitrary Blur Kernels

Zhang

Zuo

Zhang

2019

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

369

234

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While deep neural networks (DNN) based single image super-resolution (SISR) methods are rapidly gaining popularity, they are mainly designed for the widely-used bicubic degradation, and there still remains the fundamental challenge for them to super-resolve low-resolution (LR) image with arbitrary blur kernels. In the meanwhile, plug-andplay image restoration has been recognized with high flexibility due to its modular structure for easy plug-in of denoiser priors. In this paper, we propose a principled formulation and framework by extending bicubic degradation based deep SISR with the help of plug-and-play framework to handle LR images with arbitrary blur kernels. Specifically, we design a new SISR degradation model so as to take advantage of existing blind deblurring methods for blur kernel estimation. To optimize the new degradation induced energy function, we then derive a plug-and-play algorithm via variable splitting technique, which allows us to plug any super-resolver prior rather than the denoiser prior as a modular part. Quantitative and qualitative evaluations on synthetic and real LR images demonstrate that the proposed deep plug-and-play super-resolution framework is flexible and effective to deal with blurry LR images.

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“…In this subsection, various state-of-the-art learning-based super-resolution algorithms [32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49], which have been recently published, are briefly summarized in terms of key ideas. In [32], the enhanced deep super-resolution (EDSR) network based on optimization by removing an unnecessary batch normalization process was proposed.…”

Section: State-of-the-art Image Super-resolution Modelsmentioning

confidence: 99%

Trends in Super-High-Definition Imaging Techniques Based on Deep Neural Networks

Kim

Yoo

2020

Mathematics

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Images captured by cameras in closed-circuit televisions and black boxes in cities have low or poor quality owing to lens distortion and optical blur. Moreover, actual images acquired through imaging sensors of cameras such as charge-coupled devices and complementary metal-oxide-semiconductors generally include noise with spatial-variant characteristics that follow Poisson distributions. If compression is directly applied to an image with such spatial-variant sensor noises at the transmitting end, complex and difficult noises called compressed Poisson noises occur at the receiving end. The super-high-definition imaging technology based on deep neural networks improves the image resolution as well as effectively removes the undesired compressed Poisson noises that may occur during real image acquisition and compression as well as in transmission and reception systems. This solution of using deep neural networks at the receiving end to solve the image degradation problem can be used in the intelligent image analysis platform that performs accurate image processing and analysis using high-definition images obtained from various camera sources such as closed-circuit televisions and black boxes. In this review article, we investigate the current state-of-the-art super-high-definition imaging techniques in terms of image denoising for removing the compressed Poisson noises as well as super-resolution based on the deep neural networks.

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Gated Fusion Network for Degraded Image Super Resolution

Cited by 57 publications

References 81 publications

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

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

Deep Plug-And-Play Super-Resolution for Arbitrary Blur Kernels

Trends in Super-High-Definition Imaging Techniques Based on Deep Neural Networks

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