Blind dehazed image quality assessment: a deep CNN-based approach

Lv, Xiao, Xiang, Tao, Yang, Ying and Liu, Hantao ORCID: https://orcid.org/0000-0003-4544-3481 2023. Blind dehazed image quality assessment: a deep CNN-based approach. IEEE Transactions on Multimedia 25 , pp. 9410-9424. 10.1109/TMM.2023.3252267

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Abstract

Research on image dehazing has made the need for a suitable dehazed image quality assessment (DIQA) method even more urgent. The performance of existing DIQA methods heavily relies on handcrafted haze-related features. Since hazy images with uneven haze density distributions will result in uneven quality distributions after dehazing, the manually extracted feature expression is neither accurate nor robust. In this paper, we design a deep CNN-based DIQA method without a handcrafted feature requirement. Specifically, we propose a blind dehazed image quality assessment model (BDQM), which consists of three components: image preprocessing, a haze-related feature extraction network (HFNet), and an improved regression network (IRNet). In HFNet, we design a perceptual information enhancement (PIE) module to learn powerful feature representations and enhance network capability according to channel attention, multiscale convolution and residual concatenation. IRNet aims to aggregate all patch information for the quality prediction of the whole image, where the effect of inhomogeneous distortion from the dehazing procedure is attenuated via a specifically designed patch attention (PA) mechanism. Experimental results on benchmark datasets demonstrate the effectiveness and superiority of the proposed network architecture over state-of-the-art methods.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	1520-9210
Date of First Compliant Deposit:	6 March 2023
Date of Acceptance:	26 February 2023
Last Modified:	17 Jan 2024 16:42
URI:	https://orca.cardiff.ac.uk/id/eprint/157538

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