Objective Quality Evaluation of Dehazed Images

Vision-based intelligent systems like automatic driving or driving assistance can be improved by enhancing the visibility of the scenes captured in bad weather conditions. In particular, many image dehazing algorithms (DHAs) have been proposed to facilitate such applications in hazy weather. Contrary to the substantial progress of DHA developing, the quality evaluation of DHAs falls behind. Generally, DHAs can be evaluated qualitatively by human subjects or quantitatively by objective quality measures. Compared with the subjective evaluation which is time consuming and difficult to apply, objective measures with quantitative results are more needed in practical systems. But in the literature, very few measures are widely utilized, and even less measures correlate well with the overall dehazing quality (DHQ). In this paper, the authors study the DHQ evaluation using real hazy images systematically. The authors first construct a DHQ database, which is the largest of its kind so far and includes 1750 dehazed images generated from 250 real hazy images of various haze densities using seven representative DHAs. A subjective quality evaluation study is subsequently conducted on the DHQ database. Then, the authors propose an objective DHQ index (DHQI) by extracting and fusing three groups of features, including: 1) haze-removing features; 2) structure-preserving features; and 3) over-enhancement features, which have captured the most key aspects of dehazing. DHQI can be utilized to evaluate DHAs or optimize practical dehazing systems. Validations on the constructed DHQ database and three other databases with synthetic haze have verified the effectiveness of DHQI. Finally, the authors give an overview of the current DHA quality evaluation strategies, discuss their merits and demerits, and give some suggestions on systematic DHA quality evaluation. The DHQ database and the code of DHQI will be released to facilitate further research.

Language

  • English

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Filing Info

  • Accession Number: 01715794
  • Record Type: Publication
  • Files: TLIB, TRIS
  • Created Date: Aug 1 2019 1:57PM