Single image super-resolution: a comprehensive review and recent insight

Al-Mekhlafi, Hanadi; Liu, Shiguang

doi:10.1007/s11704-023-2588-9

Cited by 8 publications

(4 citation statements)

References 122 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The refinement phase consists of 4 blocks. Our DAIR implements a 4-level encoder-decoder with the number of DTB blocks from level-1 to level-4 as [4,6,6,8], the number of attention heads in MDTA as [1,2,4,8], and the number of channels as [48,96,192,384].…”

Section: Implementation Detailsmentioning

confidence: 99%

“…Many methods [1,[3][4][5][6][7] have been developed for single-image super-resolution. These methods include interpolation-based, reconstruction-based, convolutional neural networkbased, and transformer-based ones.…”

Section: Introductionmentioning

confidence: 99%

“…However, the images obtained with interpolationbased methods are too smooth and lack high-frequency information. Therefore, they are not applicable to complicated scenes that contain high-frequency details [4,6].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improving Single-Image Super-Resolution with Dilated Attention

Zhang,

Cheng,

Yang

et al. 2024

Electronics

View full text Add to dashboard Cite

Single-image super-resolution (SISR) techniques have become a vital tool for improving image quality and clarity in the rapidly evolving field of digital imaging. Convolutional neural network (CNN) and transformer-based SISR techniques are very popular. However, CNN-based techniques are not suitable when capturing long-range dependencies, and transformer-based techniques suffer from computational complexity. To tackle these problems, this paper proposes a novel method called dilated attention-based single-image super-resolution (DAIR). It comprises three components: low-level feature extraction, multi-scale dilated transformer block (MDTB), and high-quality image reconstruction. A convolutional layer is used to extract the base features from low-resolution images, which lays the foundation for subsequent processing. Dilated attention is introduced to MDTB to enhance its ability to capture image features at different scales and ensure superior image details and structure recovery. After that, MDTB refines these features to extract multi-scale global attributes and effectively grasps images’ long-distance relationships and features across multiple scales. Finally, low-level features obtained from feature extraction and multi-scale global features obtained from MDTB are aggregated to reconstruct high-resolution images. The comparison with existing methods validates the efficacy of the proposed method and demonstrates its advantage in improving image resolution and quality.

show abstract

Section: Implementation Detailsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improving Single-Image Super-Resolution with Dilated Attention

Zhang,

Cheng,

Yang

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…Image denoising, a fundamental task in computer vision [1][2][3][4], involves the restoration of clean images from noisy ones, thereby enhancing their quality. The effectiveness of denoising directly impacts various downstream tasks in computer vision applications, including super resolution [5][6][7], semantic segmentation [8][9][10], and object detection [11][12][13]. Moreover, denoising techniques play a crucial role in improving the image quality captured by diverse devices like mobile phones, reflecting the widespread demand in imaging domains.…”

Section: Introductionmentioning

confidence: 99%

A Dual-Branch Self-Boosting Network Based on Noise2Noise for Unsupervised Image Denoising

Geng,

Xu,

Xiong

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

While unsupervised denoising models have shown progress in recent years, their noise reduction capabilities still lag behind those of supervised denoising models. This limitation can be attributed to the lack of effective constraints during training, which only utilizes noisy images and hinders further performance improvements In this work, we propose a novel dual-branch self-boosting network called DBSNet, which offers a straightforward and effective approach to image denoising. By leveraging task-dependent features, we exploit the intrinsic relationships between the two branches to enhance the effectiveness of our proposed model. Initially, we extend the classic Noise2Noise (N2N) architecture by adding a new branch for noise component prediction to the existing single-branch network designed for content prediction. This expansion creates a dual-branch structure, enabling us to simultaneously decompose a given noisy image into its content (clean) and noise components. This enhancement allows us to establish stronger constraint conditions and construct more powerful loss functions to guide the training process. Furthermore, we replace the UNet structure in the N2N network with the proven DnCNN (Denoising Convolutional Neural Network) sequential network architecture, which enhances the nonlinear mapping capabilities of the DBSNet. This modification enables our dual-branch network to effectively map a noisy image to its content (clean) and noise components simultaneously. To further improve the stability and effectiveness of training, and consequently enhance the denoising performance, we introduce a feedback mechanism where the network’s outputs, i.e., content and noise components, are fed back into the dual-branch network. This results in an enhanced loss function that ensures our model possesses excellent decomposition ability and further boosts the denoising performance. Extensive experiments conducted on both synthetic and real-world images demonstrate that the proposed DBSNet outperforms the unsupervised N2N denoising model as well as mainstream supervised models trained with supervised methods. Moreover, the evaluation results on real-world noisy images highlight the desirable generalization ability of DBSNet for practical denoising applications.

show abstract

SemiRefiner: Learning to Refine Semi-realistic Paintings

Fan,

Liu

2023

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Single image super-resolution: a comprehensive review and recent insight

Cited by 8 publications

References 122 publications

Improving Single-Image Super-Resolution with Dilated Attention

Improving Single-Image Super-Resolution with Dilated Attention

A Dual-Branch Self-Boosting Network Based on Noise2Noise for Unsupervised Image Denoising

SemiRefiner: Learning to Refine Semi-realistic Paintings

Contact Info

Product

Resources

About