Enhanced Dense Space Attention Network for Super-Resolution Construction From Single Input Image

Ooi, Yoong Khang; Ibrahim, Haidi; Mahyuddin, Muhammad Nasiruddin

doi:10.1109/access.2021.3111983

Cited by 3 publications

(1 citation statement)

References 56 publications

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“…Zhang et al [66] first incorporated SE [20] with SR and pushed the stateof-the-art performance of SISR. More recent works, such as [9,21,29,43,44,58,59,61], extend this idea by adopting different spatial attention mechanisms or designing advanced attention blocks.…”

Section: Attention Based Sr Methodsmentioning

confidence: 99%

Frequency-Based Enhancement Network for Efficient Super-Resolution

Behjati¹,

Rodríguez

Fernández³

et al. 2022

IEEE Access

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Recently, deep convolutional neural networks (CNNs) have provided outstanding performance in single image super-resolution (SISR). Despite their remarkable performance, the lack of highfrequency information in the recovered images remains a core problem. Moreover, as the networks increase in depth and width, deep CNN-based SR methods are faced with the challenge of computational complexity in practice. A promising and under-explored solution is to adapt the amount of compute based on the different frequency bands of the input. To this end, we present a novel Frequency-based Enhancement Block (FEB) which explicitly enhances the information of high frequencies while forwarding low-frequencies to the output. In particular, this block efficiently decomposes features into low-and high-frequency and assigns more computation to high-frequency ones. Thus, it can help the network generate more discriminative representations by explicitly recovering finer details. Our FEB design is simple and generic and can be used as a direct replacement of commonly used SR blocks with no need to change network architectures. We experimentally show that when replacing SR blocks with FEB we consistently improve the reconstruction error, while reducing the number of parameters in the model. Moreover, we propose a lightweight SR model -Frequency-based Enhancement Network (FENet) -based on FEB that matches the performance of larger models. Extensive experiments demonstrate that our proposal performs favorably against the state-ofthe-art SR algorithms in terms of visual quality, memory footprint, and inference time. The code is available at https://github.com/pbehjatii/FENet.

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Section: Attention Based Sr Methodsmentioning

confidence: 99%

Frequency-Based Enhancement Network for Efficient Super-Resolution

Behjati¹,

Rodríguez

Fernández³

et al. 2022

IEEE Access

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Reconstruction and Super-Resolution of Land Surface Temperature Using an Attention-Enhanced CNN Architecture

Daniels,

Bailey

2023

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

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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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Enhanced Dense Space Attention Network for Super-Resolution Construction From Single Input Image

Cited by 3 publications

References 56 publications

Frequency-Based Enhancement Network for Efficient Super-Resolution

Frequency-Based Enhancement Network for Efficient Super-Resolution

Reconstruction and Super-Resolution of Land Surface Temperature Using an Attention-Enhanced CNN Architecture

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

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