DHIQA: Quality assessment of dehazed images based on attentive multi-scale feature fusion and rank learning

Abstract

Haze is a ubiquitous atmospheric phenomenon that seriously influences the visibility of images. To this end, numerous image dehazing models have been proposed to improve the visual quality of hazy images. However, the quality assessment of dehazed images has fallen behind. Traditional IQA methods for dehazed images rely on complex handcrafted features that may not accurately evaluate the quality of dehazed images. In this paper, we present a novel no-reference image quality assessment network for dehazed images (called DHIQA). Firstly, we propose a multi-scale feature fusion network that considers typical dehazed distortion-related features, such as contrast, dark channel, edge, etc. Then, inspired by the contrast sensitivity function of the Human Visual System (HVS), we propose a contrast attentive module to enhance the effectiveness of the proposed IQA network. Moreover, in addition to the absolute quality score of dehazed images, their rankings are also important for quality assessment. Thus, we propose a new ranking loss, which takes both the absolute quality score and their rankings into consideration. Extensive experimental results on six publicly available dehazed IQA databases show that the proposed model achieves superior performance compared to current state-of-the-art metrics.