  Image dehazing aims to restore spatial details from hazy images. There have
emerged a number of image dehazing algorithms, designed to increase the
visibility of those hazy images. However, much less work has been focused on
evaluating the visual quality of dehazed images. In this paper, we propose a
Reduced-Reference dehazed image quality evaluation approach based on Partial
Discrepancy (RRPD) and then extend it to a No-Reference quality assessment
metric with Blind Perception (NRBP). Specifically, inspired by the hierarchical
characteristics of the human perceiving dehazed images, we introduce three
groups of features: luminance discrimination, color appearance, and overall
naturalness. In the proposed RRPD, the combined distance between a set of
sender and receiver features is adopted to quantify the perceptually dehazed
image quality. By integrating global and local channels from dehazed images,
the RRPD is converted to NRBP which does not rely on any information from the
references. Extensive experiment results on several dehazed image quality
databases demonstrate that our proposed methods outperform state-of-the-art
full-reference, reduced-reference, and no-reference quality assessment models.
Furthermore, we show that the proposed dehazed image quality evaluation methods
can be effectively applied to tune parameters for potential image dehazing
algorithms.
