Adaptive Densely Connected Single Image Super-Resolution

Xie, Tangxin; Yang, Xin; Jia, Yu; Zhu, Chen; Li, Xiaochuan

doi:10.1109/iccvw.2019.00426

Cited by 13 publications

(6 citation statements)

References 33 publications

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“…The DSSR [75] proposed by NUAA-404 achieves superiority in generating a highfidelity track, and the specific network structure is shown in Figure 17. The network connects two × 4 networks to achieve the target result of ×16.…”

Section: Dssr/mgbpv2 (Winner Of Aim 2019)mentioning

confidence: 99%

Single-Image Super-Resolution Challenges: A Brief Review

Zhao

et al. 2023

Electronics

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Single-image super-resolution (SISR) is an important task in image processing, aiming to achieve enhanced image resolution. With the development of deep learning, SISR based on convolutional neural networks has also gained great progress, but as the network deepens and the task of SISR becomes more complex, SISR networks become difficult to train, which hinders SISR from achieving greater success. Therefore, to further promote SISR, many challenges have emerged in recent years. In this review, we briefly review the SISR challenges organized from 2017 to 2022 and focus on the in-depth classification of these challenges, the datasets employed, the evaluation methods used, and the powerful network architectures proposed or accepted by the winners. First, depending on the tasks of the challenges, the SISR challenges can be broadly classified into four categories: classic SISR, efficient SISR, perceptual extreme SISR, and real-world SISR. Second, we introduce the datasets commonly used in the challenges in recent years and describe their characteristics. Third, we present the image evaluation methods commonly used in SISR challenges in recent years. Fourth, we introduce the network architectures used by the winners, mainly to explore in depth where the advantages of their network architectures lie and to compare the results of previous years’ winners. Finally, we summarize the methods that have been widely used in SISR in recent years and suggest several possible promising directions for future SISR.

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Section: Dssr/mgbpv2 (Winner Of Aim 2019)mentioning

confidence: 99%

Single-Image Super-Resolution Challenges: A Brief Review

Zhao

et al. 2023

Electronics

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“…Following [8] and [24], we use DIV2K [25] and Flickr2K [7] as our training data. We adopt data augmentation as [7] does.…”

Section: A Datasets and Metricsmentioning

confidence: 99%

A Wavelet-Based Asymmetric Convolution Network for Single Image Super-Resolution

et al. 2021

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Recently, deep convolutional neural networks (CNNs) have been widely explored in single image super-resolution(SISR) and obtained remarkable performance. However, most of the existing CNNbased SISR methods tend to produce over-smoothed outputs and miss some textural details. To address these issues, we propose a wavelet-based asymmetric convolution network (WACN). Different from conventional CNN methods that directly infer HR images, our approach firstly learns to predict the LR's corresponding series of HR's wavelet coefficients before reconstructing HR images from them. This helps to capture more structural information in images to preserve texture information and avoid artifacts. To enhance the ability of feature extraction, we propose an asymmetric convolution block (ACB) structure to form a very deep network. In the training phase, ACB can provide different receptive fields to enrich feature information. In the inference phase, ACB's asymmetric convolution kernel can be equivalently fused into the standard square-kernel layers, such that no extra computational burdens are introduced in the inference phase. Furthermore, we propose a variance-based channel attention (VCA) mechanism to adaptively rescale channel-wise features by considering interdependencies among channels. Extensive experimental results demonstrate the superiority of the proposed WACN in comparison with the state-ofthe-art methods.

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“…The CETC-CSKT team proposed Adaptive Dense Connection Super Resolution reconstruction(ADCSR) [35]. The algorithm is divided into BODY and SKIP.…”

Section: Cetc-csktmentioning

confidence: 99%

AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results

Wei

Timofte

et al. 2020

Computer Vision – ECCV 2020 Workshops

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This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020. This challenge involves three tracks to super-resolve an input image for ×2, ×3 and ×4 scaling factors, respectively. The goal is to attract more attention to realistic image degradation for the SR task, which is much more complicated and challenging, and contributes to real-world image super-resolution applications. 452 participants were registered for three tracks in total, and 24 teams submitted their results. They gauge the state-of-the-art approaches for real image SR in terms of PSNR and SSIM.

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Adaptive Densely Connected Single Image Super-Resolution

Cited by 13 publications

References 33 publications

Single-Image Super-Resolution Challenges: A Brief Review

Single-Image Super-Resolution Challenges: A Brief Review

A Wavelet-Based Asymmetric Convolution Network for Single Image Super-Resolution

AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results

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