SAD-Net: a full spectral self-attention detail enhancement network for single image dehazing
Abstract Single-image dehazing technology plays a significant role in video surveillance and intelligent transportation. However, existing dehazing methods using vanilla convolution only extract features in the temporal domain and lack the ability to capture multi-directional information. To address...
Saved in:
| Main Authors: | Qingjun Niu, Kun Wu, Jialu Zhang, Zhenqi Han, Lizhuang Liu |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-92061-1 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Alpha-DehazeNet: single image dehazing via RGBA haze modeling and adaptive learning
by: Jin He, et al.
Published: (2025-07-01) -
MLKD-Net: Lightweight Single Image Dehazing via Multi-Head Large Kernel Attention
by: Jiwon Moon, et al.
Published: (2025-05-01) -
Image dehazing based on double branch convolution and detail enhancement
by: ZHAI Fengwen, et al.
Published: (2025-02-01) -
DWTMA-Net: Discrete Wavelet Transform and Multi-Dimensional Attention Network for Remote Sensing Image Dehazing
by: Xin Guan, et al.
Published: (2025-06-01) -
DefogNet: A Single-Image Dehazing Algorithm with Cyclic Structure and Cross-Layer Connections
by: Suting Chen, et al.
Published: (2021-01-01)