NO REFERENCE QUALITY OF THE   HAZY IMAGES DEPENDING ON TRANSMISSION COMPONENT ESTIMATION

Hana H Kareem; Esraa G. Daway; hazim G. Daway

doi:10.31436/iiumej.v21i1.1006

Authors

Hana H Kareem https://orcid.org/0000-0003-2729-5884
Esraa G. Daway
hazim G. Daway uomustansiriyah.edu.iq https://orcid.org/0000-0002-8278-3349

DOI:

https://doi.org/10.31436/iiumej.v21i1.1006

Keywords:

no reference quality; wavelet transform; hazy images; transmission

Abstract

The research aim is to measure the quality of hazy images using a no-reference scale based on the Transmission Component and Wavelet Transform (TCWT) by calculating the histogram in the High and Low (HL) component. The system is designed to capture several images at different levels of distortion from little to medium to high and the quality is studied in the transmission component. This measure is compared with the other no-reference measurements as a Haze Distribution Map based Haze Assessment (HDMHA) and Entropy by calculating the correlation coefficient between the no reference measurements and the reference scale Universal Quality Index (UQI). The results show that the proposed algorithm TCWT is a good measure of the quality of hazy images.

ABSTRAK: Kajian ini bertujuan bagi mengukur kualiti imej berjerebu dengan menggunakan skala tiada-rujukan berdasarkan Komponen Transmisi dan Penukaran Signal Gelombang (TCWT) dengan mengira komponen Tinggi dan Rendah (HL) histogram. Sistem ini dicipta bagi mengumpul imej pada tahap berbeza dari takat selerakan paling rendah kepada paling tinggi dan kualiti imej diselidik dalam komponen transmisi. Ukuran ini dibandingkan dengan ukuran tiada-rujukan lain sebagai Peta Selerak Berjerebu (UQI). Keputusan menunjukkan algoritma kualiti imej berjerebu TCWT yang dicadangkan adalah berkualiti baik.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

References

Aber, J. S., Marzolff, I., & Ries, J. B. (2010). Small-format aerial photography. Boston: Elsevier. 266.

Murphy, B. L., and Morrison, R. D. (2002). Introduction to Environ-mental Forensics . San Diego, CA: Academic Press.

Abdou IE, Dusaussoy NJ. (1986) Survey of image quality measurements. Paper presented at Proceedings of 1986 ACM Fall joint computer conference, IEEE Computer Society Press. pp71-78.

Yu Z, Wu HR, Winkler S, Chen T. (2002) Vision-model-based impairment metric to evaluate blocking artifacts in digital video. Proceedings of the IEEE, 90(1): 154-169.

Nill NB, Bouzas B. (1992) Objective image quality measure derived from digital image power spectra. Optical Engineering, 31(4): 813-826.

Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. (2004) Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4): 600-612.

Wang Z, Bovik AC. (2002) A universal image quality index. IEEE Signal Processing Letters, 9(3): 81-84.

Lu Y, Xie F, Wu Y, Jiang Z, Meng R. (2015) No reference uneven illumination assessment for dermoscopy images. IEEE Signal Processing Letters, 22(5): 534-538.

Cao Z, Wei Z, Zhang G. (2014) A no-reference sharpness metric based on the notion of relative blur for Gaussian blurred image. Journal of Visual Communication and Image Representation, 25(7): 1763-1773.

Corchs S, Gasparini F, Schettini R. (2014) No reference image quality classification for JPEG-distorted images. Digital Signal Processing, 30: 86-100.

Liang L, Wang S, Chen J, Ma S, Zhao D, Gao W. (2010) No-reference perceptual image quality metric using gradient profiles for JPEG2000. Signal Processing: Image Communication, 25(7): 502-516.

Liu M, Zhai G, Zhang Z, Sun Y, Gu K, Yang X. (2014) Blind image quality assessment for noise, paper presented at Broadband Multimedia Systems and Broadcasting (BMSB), 2014 IEEE International Symposium on, IEEE, 1–5.

Moorthy AK, Bovik AC. (2010) A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters, 17(5): 513-516.

Tang H, Joshi N, Kapoor A. (2011) Learning a blind measure of perceptual image quality, paper presented at Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, IEEE.

Thuy Tuong Nguyen, Xuan Dai Pham, Dongkyun Kim and Jae Wook Jeon, (2008,"Automatic Exposure Compensation for Line Detection Applications",IEEE International Conference on Multisensor Fusion and Integration for Intelligent SystemsSeoul, Korea.

Pan X, Xie F, Jiang Z, Shi Z, Luo X. (2016) No-reference assessment on haze for remote-sensing images. IEEE Geoscience and Remote Sensing Letters, 13(12): 1855-1859.

Daway, H.G.; Mohammed, F.S.; Abdulabbas, D.A. (2016) Aerial Image Enhancement Using Modified Fast Visibility Restoration Based on Sigmoid Function. Adv. Nat. Appl. Sci. 2016, 10 (11), 16–22

He K, Sun J, Tang X. (2011) Single image haze removal using dark channel prior. IEEE transactions on pattern analysis and machine intelligence, 33(12): 2341-2353.

Tan RT. (2008) Visibility in bad weather from a single image, paper presented at Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, IEEE.

Wang Z, Feng Y. (2014) Fast single haze image enhancement. Computers & Electrical Engineering, 40(3): 785-795.

Gonzalez RC, Woods RE. (2002) Digital image processing, edited, Prentice hall New Jersey