Residual trio feature network for efficient super-resolution
Abstract Deep learning-based approaches have demonstrated impressive performance in single-image super-resolution (SISR). Efficient super-resolution compromises the reconstructed image’s quality to have fewer parameters and Flops. Ensured efficiency in image reconstruction and improved reconstructio...
Saved in:
Main Authors: | Junfeng Chen, Mao Mao, Azhu Guan, Altangerel Ayush |
---|---|
Format: | Article |
Language: | English |
Published: |
Springer
2024-11-01
|
Series: | Complex & Intelligent Systems |
Subjects: | |
Online Access: | https://doi.org/10.1007/s40747-024-01624-8 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Gradient pooling distillation network for lightweight single image super-resolution reconstruction
by: Zhiyong Hong, et al.
Published: (2025-02-01) -
MDRN: Multi-distillation residual network for efficient MR image super-resolution
by: Liwei Deng, et al.
Published: (2024-10-01) -
Enhancing the Spatial Resolution of Sentinel-2 Images Through Super-Resolution Using Transformer-Based Deep-Learning Models
by: Alireza Sharifi, et al.
Published: (2025-01-01) -
Fluorescence engineering in metamaterial-assisted super-resolution localization microscope
by: Choi Kyu Ri, et al.
Published: (2023-03-01) -
Super-resolution microscopy: Shedding new light on blood cell imaging
by: Huan Deng, et al.
Published: (2025-01-01)