Residual Feature Aggregation Network for Image Super-Resolution

By Jie Liu, Wenjie Zhang, Yu Tang, Jie Tang, Gangshan Wu. Computer Vision and Pattern Recognition, 2020

514 citations recorded by the source corpus.

Recently, very deep convolutional neural networks (CNNs) have shown great power in single image super-resolution (SISR) and achieved significant improvements against traditional methods. Among these CNN-based methods, the residual connections play a critical role in boosting the network performance. As the network depth grows, the residual features gradually focused on different aspects of the input image, which is very useful for reconstructing the spatial details. However, existing methods neglect to fully utilize the hierarchical features on the residual branches. To address this issue, we propose a novel residual feature aggregation (RFA) framework for more efficient feature extraction. The RFA framework groups several residual modules together and directly forwards the features on each local residual branch by adding skip connections. Therefore, the RFA framework is capable of aggregating these informative residual features to produce more representative features. To maximize the power of the RFA framework, we further propose an enhanced spatial attention (ESA) block to make the residual features to be more focused on critical spatial contents. The ESA block is designed to be lightweight and efficient. Our final RFANet is constructed by applying the proposed RFA framework with the ESA blocks. Comprehensive experiments demonstrate the necessity of our RFA framework and the superiority of our RFANet over state-of-the-art SISR methods.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/8a8a5f327ead63fa56d72e8e75a647e3e6154bc8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Radu Timofte
3. Chen Change Loy
4. Ming-Hsuan Yang
5. Lei Zhang
6. Kai Zhang
7. Chao Dong
8. Yu Qiao

In Media Technology

1. Radu Timofte
2. Luc Van Gool
3. Kai Zhang
4. Jingyun Liang
5. Jinjin Gu
6. Chao Dong
7. Yulun Zhang
8. Xintao Wang

Citations in the corpus

• Towards Real-World Blind Face Restoration with Generative Facial Prior (2021, background): “Image Restoration typically includes super-resolution [14, 50, 62, 51, 75, 10, 23, 52], denoising [72, 44, 27], deblurring [67, 40, 60] and compression removal [13, 22].”
• Residual Feature Distillation Network for Lightweight Image Super-Resolution (2020, methodology): “Specifically, we replace the CCA layer in RFDB with the ESA block [18] and we use 4 such enhanced RFDBs (E-RFDBs) in E-RFDN.”
• Real-World Single Image Super-Resolution: A Brief Review (2021, methodology): “Overall, the SR reconstruction performance on synthetic LR images is rather good, especially for deep learning-based SISR approaches such as RCAN [73], SAN [75], and RFANet [77].”
• Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling (2021, methodology): “Since the pioneer work SRCNN [9], many CNNbased models have been proposed in recent years [9, 19, 24, 43, 41, 40, 42, 15, 6, 6, 54, 55, 30, 49, 11, 5, 44, 26, 51, 27, 50].”
• Dual Aggregation Transformer for Image Super-Resolution (2023, methodology): “We compare our models: DAT-S, DAT-2, and DAT, with state-of-the-art methods: EDSR [14], SRMDNF [27], RDN [32], OISR [9], RCAN [30], NLRN [15], RNAN [31], SRFBN [12], SAN [5], RFANet [16], IGNN [34], HAN [23], CSNLN [22], NLSA [21], CRAN [33], ELAN [29], DFSA [17], SwinIR [13], and CAT-A [4].”