TY - JOUR
T1 - Evaluation of the quality indicators in dehazed images
T2 - Color, contrast, naturalness, and visual pleasingness
AU - Rahadianti, Laksmita
AU - Azizah, Aruni Yasmin
AU - Deborah, Hilda
N1 - Funding Information:
This research was funded by Universitas Indonesia through Hi-bah Publikasi Terindeks Internasional (PUTI) Q3 grant number NKB- 1822/UN2.RST/HKP.05.00/2020. NTNU collaboration is supported by FRIPRO FRINATEK Metrological texture analysis for hyperspectral images (project nr. 274881) funded by the Research Council of Norway.
Publisher Copyright:
© 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/li-censes/by-nc-nd/4.0/)
PY - 2021/9
Y1 - 2021/9
N2 - Hazy images suffer from low quality due to blurring, veiling effects, and low contrast. To improve their visibility, dehazing methods attempt to restore them to their corresponding clear scenes, often by focusing more on obtaining an accurate estimate based on a known ground truth. The perceptual quality of dehazed images, which can be described by means of objective and subjective quality assessments, is often not considered. This paper provides a quality assessment of dehazed images, focusing on aspects, e.g., color, image structure, and naturalness. Four image dehazing methods are considered, i.e., Contrast Limited Adapted Histogram Equalization (CLAHE), Dark Channel Prior and Refinement (DCP-R), Perception Inspired Deep Dehazing Network with Refinement (PDR-Net) and Conditional Generative Adversarial Network (CGAN) Pix2pix. The dehazing results are then put through objective and subjective assessments, for a comprehensive evaluation on image quality. Overall, Pix2pix shows the best results objectively, excelling in the recovery of color and image structure. Although it is outperformed by DCP-R in terms of naturalness, our subjective assessment shows that Pix2pix is also most preferred by human observers
AB - Hazy images suffer from low quality due to blurring, veiling effects, and low contrast. To improve their visibility, dehazing methods attempt to restore them to their corresponding clear scenes, often by focusing more on obtaining an accurate estimate based on a known ground truth. The perceptual quality of dehazed images, which can be described by means of objective and subjective quality assessments, is often not considered. This paper provides a quality assessment of dehazed images, focusing on aspects, e.g., color, image structure, and naturalness. Four image dehazing methods are considered, i.e., Contrast Limited Adapted Histogram Equalization (CLAHE), Dark Channel Prior and Refinement (DCP-R), Perception Inspired Deep Dehazing Network with Refinement (PDR-Net) and Conditional Generative Adversarial Network (CGAN) Pix2pix. The dehazing results are then put through objective and subjective assessments, for a comprehensive evaluation on image quality. Overall, Pix2pix shows the best results objectively, excelling in the recovery of color and image structure. Although it is outperformed by DCP-R in terms of naturalness, our subjective assessment shows that Pix2pix is also most preferred by human observers
KW - Dehazing
KW - Hazy images
KW - Image quality
KW - Image restoration
KW - Psychovisual experiment
UR - http://www.scopus.com/inward/record.url?scp=85120905127&partnerID=8YFLogxK
U2 - 10.1016/j.heliyon.2021.e08038
DO - 10.1016/j.heliyon.2021.e08038
M3 - Article
AN - SCOPUS:85120905127
SN - 2405-8440
VL - 7
JO - Heliyon
JF - Heliyon
IS - 9
M1 - e08038
ER -