Improving Medical Image Quality Using a Super-Resolution Technique with Attention Mechanism
Image quality plays a critical role in medical image analysis, significantly impacting diagnostic outcomes. Sharp and detailed images are essential for accurate diagnoses, but acquiring high-resolution medical images often demands sophisticated and costly equipment. To address this challenge, this s...
Saved in:
Main Authors: | Dong Yun Lee, Jang Yeop Kim, Soo Young Cho |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2025-01-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/15/2/867 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Feature enhanced cascading attention network for lightweight image super-resolution
by: Feng Huang, et al.
Published: (2025-01-01) -
A Lightweight CNN-Transformer Implemented via Structural Re-Parameterization and Hybrid Attention for Remote Sensing Image Super-Resolution
by: Jie Wang, et al.
Published: (2024-12-01) -
MFCEN: A lightweight multi-scale feature cooperative enhancement network for single-image super-resolution
by: Jiange Liu, et al.
Published: (2024-10-01) -
IGAF: Incremental Guided Attention Fusion for Depth Super-Resolution
by: Athanasios Tragakis, et al.
Published: (2024-12-01) -
Single Infrared Super-Resolution via a Shifted Full-Scale Non-Local Network
by: Honghong Lu, et al.
Published: (2024-01-01)