Name
Title | ||
|---|---|---|
MISNet: Multi-resolution Level Feature Interpolating Ultralight-weight Residual Image Super Resolution Network |
Abstract | ||
|---|---|---|
The design of ultralight-weight super-resolution convolutional neural networks capable of providing images with high visual quality is crucial in many real-world applications with limited power and storage capacity, such as mobile devices and portable cameras. In this paper, a new ultralight-weight super-resolution network, based on the idea of using multi-resolution level feature interpolation in a residual framework, is developed. In the proposed network, the multiple resolution level interpolated features generated are fused and the resulting feature maps are added to the residual features obtained from a shallow convolutional neural network. The proposed network is applied to various benchmark datasets and is shown to outperform the state-of-the-art ultralight-weight image super-resolution networks existing in the literature. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ISCAS51556.2021.9401641 | 2021 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS) |
Keywords | DocType | ISSN |
Image Super Resolution, Deep Learning, Residual Learning, Image Interpolation, Image Fusion | Conference | 0271-4302 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alireza Esmaeilzehi | 1 | 1 | 3.73 |
| M. O. Ahmad | 2 | 1157 | 154.87 |
| M. N. Swamy | 3 | 104 | 18.85 |