Volatile-Nonvolatile Memory Network for Progressive Image Super-Resolution

Choi, Jun‐Ho; Kim, Junhyuk; Cheon, Manri; Lee, Jong-Seok

doi:10.1109/access.2021.3063760

Cited by 3 publications

(1 citation statement)

References 25 publications

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“…Shi et al [25] presented the efficient sub-pixel convolutional neural network (ESPCN), whose computational cost is low enough to accomplish super-resolution in real time. Choi et al [26] presented the volatile-nonvolatile memory network (VMNet), which utilises shared weights between CNN layers to optimise model efficiency.…”

Section: Related Studymentioning

confidence: 99%

An Improved Scene Text Detection approach by utilising a Dual Discriminator framework

Pattanaik

Balabantaray

2023

Preprint

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Deep learning’s emergence and continued advancement have significantlychanged and redefined computer vision. Scene text identification andrecognition, a significant area of computer vision research, has unavoidablybeen impacted by this wave of innovation, and consequently hasentered the era of deep learning.It has a wide range of practical uses,from semantic natural scene analysis to navigation for those with visualimpairments. The complexity of the background, the image quality, thetext orientation, the text size, etc. present obstacles to experts in thefield of scene text analysis. Our proposed model provides a solution forscene text localization, detection, and refinement by integrating featurebasedand deep learning-based approaches. To obtain high quality andclear images, our model super-resolves images and deblurs them usingan Edge Attention Network (EAN). It is followed by a candidate regionproposal module that uses Maximally Stable Extremal Region (MSER)and Stroke Width Transform (SWT) to create text region suggestions.The detection result is then improved using a post-processing methodthat uses the idea of a Generative Adversarial Network (GAN). Standarddatasets including ICDAR 2013, ICDAR 2015, and the Street View Text(SVT) have been used for experiments verification which concludes thatour proposed model consistently produces acceptable results.

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Section: Related Studymentioning

confidence: 99%

An Improved Scene Text Detection approach by utilising a Dual Discriminator framework

Pattanaik

Balabantaray

2023

Preprint

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Edge Attention Network for Image Deblurring and Super-Resolution

Han

Choi

Kim

et al. 2021

2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Recurrent Multi-scale Approximation-Guided Network for Single Image Super-Resolution

Hsu

Jian

2023

ACM Trans. Multimedia Comput. Commun. Appl.

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Single-image super-resolution (SISR) is an essential topic in computer vision applications. However, most CNN-based SISR approaches directly learn the relationship between low- and high-resolution images while ignoring the contextual texture and detail fidelity to explore super-resolution; thus, they hinder the representational power of CNNs and lead to the unrealistic, distorted reconstruction of edges and textures in the images. In this study, we propose a novel recurrent structure preservation mechanism with the integration and innovative use of multi-scale wavelet transform, Recurrent Multiscale Approximation-guided Network (RMANet), to recursively process the low-frequency and high-frequency sub-networks at each level separately. Unlike traditional wavelet transform, we propose a novel Approximation Level Preservation (ALP) architecture to import and learn the low-frequency sub-networks at each level. Through proposed Approximation level fusion (ALF) and inverse wavelet transform, rich image structures of low frequency at each level can be recursively restored and greatly preserved with the combination of ALP at each level. In addition, a novel low-frequency to high-frequency detail enhancement (DE) mechanism is also proposed to solve the problem of detail distortion in high-frequency networks by transmitting low-frequency information to the high-frequency network. Finally, a joint loss function is used to balance low-frequency and high-frequency information with different degrees of fusion. In addition to correct restoration, image details are further enhanced by tuning different hyperparameters during training. Compared with the state-of-the-art approaches, the experimental results on synthetic and real datasets demonstrate that the proposed RMANet achieves better performance in visual presentation, especially in image edges and texture details.

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Volatile-Nonvolatile Memory Network for Progressive Image Super-Resolution

Cited by 3 publications

References 25 publications

An Improved Scene Text Detection approach by utilising a Dual Discriminator framework

An Improved Scene Text Detection approach by utilising a Dual Discriminator framework

Edge Attention Network for Image Deblurring and Super-Resolution

Recurrent Multi-scale Approximation-Guided Network for Single Image Super-Resolution

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