Image Super-Resolution via Progressive Cascading Residual Network

Ahn, Namhyuk; Kang, Bada; Sohn, Kyung-Ah

doi:10.1109/cvprw.2018.00123

Cited by 212 publications

(43 citation statements)

References 20 publications

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“…ProSR ( Wang et al, 2018a ) uses the upsampled output of the previous level and linearly trains the next level using the previous one, while ADRSR ( Bei et al, 2018 ) concatenates the HR output of the previous levels and further adds another convolution layer. In CARN ( Ahn, Kang & Sohn, 2018b ), the previously generated image is entirely replaced by the next level generated image, updating the HR image in sequential order.…”

Section: Supervised Super-resolutionmentioning

confidence: 99%

A comprehensive review of deep learning-based single image super-resolution

Bashir

Wang

Khan

et al. 2021

PeerJ Computer Science

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Image super-resolution (SR) is one of the vital image processing methods that improve the resolution of an image in the field of computer vision. In the last two decades, significant progress has been made in the field of super-resolution, especially by utilizing deep learning methods. This survey is an effort to provide a detailed survey of recent progress in single-image super-resolution in the perspective of deep learning while also informing about the initial classical methods used for image super-resolution. The survey classifies the image SR methods into four categories, i.e., classical methods, supervised learning-based methods, unsupervised learning-based methods, and domain-specific SR methods. We also introduce the problem of SR to provide intuition about image quality metrics, available reference datasets, and SR challenges. Deep learning-based approaches of SR are evaluated using a reference dataset. Some of the reviewed state-of-the-art image SR methods include the enhanced deep SR network (EDSR), cycle-in-cycle GAN (CinCGAN), multiscale residual network (MSRN), meta residual dense network (Meta-RDN), recurrent back-projection network (RBPN), second-order attention network (SAN), SR feedback network (SRFBN) and the wavelet-based residual attention network (WRAN). Finally, this survey is concluded with future directions and trends in SR and open problems in SR to be addressed by the researchers.

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Section: Supervised Super-resolutionmentioning

confidence: 99%

A comprehensive review of deep learning-based single image super-resolution

Bashir

Wang

Khan

et al. 2021

PeerJ Computer Science

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show abstract

“…However, these methods do not reproduce the sharp details present in natural images due to their reliance on L 2 and L 1 reconstruction losses. This was addressed in URDGN [53], SRGAN [22] and more recent approaches [2,15,38,46] by adopting a conditional GAN based architecture and training strategy. While these works aim to predict one example, we undertake the more ambitious goal of learning the distribution of all plausible reconstructions from the natural image manifold.…”

Section: Related Workmentioning

confidence: 99%

SRFlow: Learning the Super-Resolution Space with Normalizing Flow

Lugmayr

Danelljan

Gool

et al. 2020

Lecture Notes in Computer Science

252

250

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Super-resolution is an ill-posed problem, since it allows for multiple predictions for a given low-resolution image. This fundamental fact is largely ignored by state-of-the-art deep learning based approaches. These methods instead train a deterministic mapping using combinations of reconstruction and adversarial losses. In this work, we therefore propose SRFlow: a normalizing flow based super-resolution method capable of learning the conditional distribution of the output given the lowresolution input. Our model is trained in a principled manner using a single loss, namely the negative log-likelihood. SRFlow therefore directly accounts for the ill-posed nature of the problem, and learns to predict diverse photo-realistic high-resolution images. Moreover, we utilize the strong image posterior learned by SRFlow to design flexible image manipulation techniques, capable of enhancing super-resolved images by, e.g., transferring content from other images. We perform extensive experiments on faces, as well as on super-resolution in general. SRFlow outperforms state-of-the-art GAN-based approaches in terms of both PSNR and perceptual quality metrics, while allowing for diversity through the exploration of the space of super-resolved solutions. Code: git.io/Jfpyu.

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“…In improving the SISR performance, cascading multistage networks can improve the resolution step by step [3]. A coarse-to-fine CNN [53] uses heterogeneous convolutions in a stack of feature extraction blocks to extract low-frequency features, then, applies feature refinement block to learn more accurate high-frequency features for image-resolution.…”

Section: Deep Cnns Based Cascaded Structures For Sisrmentioning

confidence: 99%

Lightweight image super-resolution with enhanced CNN

Tian

Zhuge

et al. 2020

Knowledge-Based Systems

120

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Deep convolutional neural networks (CNNs) with strong expressive ability have achieved impressive performances on single image super-resolution (SISR). However, their excessive amounts of convolutions and parameters usually consume high computational cost and more memory storage for training a SR model, which limits their applications to SR with resource-constrained devices in real world. To resolve these problems, we propose a lightweight enhanced SR CNN (LESR-CNN) with three successive sub-blocks, an information extraction and enhancement block (IEEB), a reconstruction block (RB) and an information refinement block (IRB). Specifically, the IEEB extracts hierarchical low-resolution (LR) features and aggregates the obtained features step-by-step to increase the memory ability of the shallow layers on deep layers for SISR. To remove redundant information obtained, a heterogeneous architecture is adopted in the IEEB. After that, the RB converts low-frequency features into high-frequency features by fusing global and local features, which is complementary with the IEEB in tackling the long-term dependency problem. Finally, the IRB uses coarse high-frequency features from the RB to learn more accurate SR features and construct a SR image. The proposed LESRCNN can obtain a high-quality image by a model for different scales. Extensive experiments demonstrate that the proposed LESRCNN outperforms state-of-the-arts on SISR in terms of qualitative and quantitative evaluation. The code of LESR-CNN is accessible on https://github.com/hellloxiaotian/LESRCNN.

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Image Super-Resolution via Progressive Cascading Residual Network

Cited by 212 publications

References 20 publications

A comprehensive review of deep learning-based single image super-resolution

A comprehensive review of deep learning-based single image super-resolution

SRFlow: Learning the Super-Resolution Space with Normalizing Flow

Lightweight image super-resolution with enhanced CNN

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