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Dual-Stream Complex-Valued Convolutional Network for Authentic Dehazed Image Quality Assessment.
- Source :
- IEEE Transactions on Image Processing; 2024, Vol. 33, p466-478, 13p
- Publication Year :
- 2024
-
Abstract
- Effectively evaluating the perceptual quality of dehazed images remains an under-explored research issue. In this paper, we propose a no-reference complex-valued convolutional neural network (CV-CNN) model to conduct automatic dehazed image quality evaluation. Specifically, a novel CV-CNN is employed that exploits the advantages of complex-valued representations, achieving better generalization capability on perceptual feature learning than real-valued ones. To learn more discriminative features to analyze the perceptual quality of dehazed images, we design a dual-stream CV-CNN architecture. The dual-stream model comprises a distortion-sensitive stream that operates on the dehazed RGB image, and a haze-aware stream on a novel dark channel difference image. The distortion-sensitive stream accounts for perceptual distortion artifacts, while the haze-aware stream addresses the possible presence of residual haze. Experimental results on three publicly available dehazed image quality assessment (DQA) databases demonstrate the effectiveness and generalization of our proposed CV-CNN DQA model as compared to state-of-the-art no-reference image quality assessment algorithms. [ABSTRACT FROM AUTHOR]
- Subjects :
- CONVOLUTIONAL neural networks
PERCEPTUAL illusions
PERCEPTUAL learning
Subjects
Details
- Language :
- English
- ISSN :
- 10577149
- Volume :
- 33
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Image Processing
- Publication Type :
- Academic Journal
- Accession number :
- 174718019
- Full Text :
- https://doi.org/10.1109/TIP.2023.3343029