2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDTEC-4.6
Paper Title A FAST AND EFFICIENT SUPER-RESOLUTION NETWORK USING HIERARCHICAL DENSE RESIDUAL LEARNING
Authors Vinh Van Duong, Thuc Nguyen Huu, Jonghoon Yim, Byeungwoo Jeon, Sungkyunkwan University, Republic of Korea
SessionTEC-4: Super-resolution
LocationArea G
Session Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Poster
Topic Image and Video Processing: Interpolation, super-resolution, and mosaicing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract In deep convolutional neural networks (DCNNs) for single image super-resolution (SISR), the dense and residual feature refinement helps to stabilize the training network and enriches the feature values. However, most SISR networks do not fully exploit the rich feature information in the hierarchical dense residual connections, thus achieving relatively low performance. Besides, in many cases, a large model is not feasible to deploy on mobile or embedded devices. By exploiting the hierarchical dense residual learning, this paper proposes a fast and efficient hierarchical dense residual network (HDRN) to solve these problems. Specifically, we develop a dense compact residual group (DCRG), consisting of several compact residual blocks (CRB), by using multi-level residual and dense skip connections. Our experimental results confirm that the proposed HDRN achieves a better trade-off between the performance and computational costs than those state-of-the-art lightweight SISR methods.