A Lightweight Neural Network for Denoising Wrapped-Phase Images Generated with Full-Field Optical Interferometry
Phase wrapping is a common phenomenon in optical full-field imaging or measurement systems. It arises from large phase retardations and results in wrapped-phase maps that contain essential information about surface roughness and topology. However, these maps are often degraded by noise, such as spec...
Saved in:
| Main Authors: | Muhammad Awais, Younggue Kim, Taeil Yoon, Wonshik Choi, Byeongha Lee |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/10/5514 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Multi-convolutional neural network brain image denoising study based on feature distillation learning and dense residual attention
by: Huimin Qu, et al.
Published: (2025-03-01) -
Model-Based Deep Network for Single Image Deraining
by: Pengyue Li, et al.
Published: (2020-01-01) -
A novel mosaic adaptive attention network for multispectral imaging reconstruction
by: Zhongqiang Zhang, et al.
Published: (2025-12-01) -
A Novel Attention-Guided Enhanced U-Net With Hybrid Edge-Preserving Structural Loss for Low-Dose CT Image Denoising
by: Muhammad Zubair, et al.
Published: (2025-01-01) -
Dense-Fusion2Net a more efficient and lightweight short speech speaker recognition system with time-frequency channel attention
by: Fei Deng, et al.
Published: (2025-03-01)