Development and Implementation of Software for Multi-Algorithm Image Quality Enhancement

Xiyuan Luo

Abstract


Different image defogging methods should be adopted for different degrees of low contrast and sharpness of images caused by different weather conditions. This paper builds a MATLAB-based image quality improvement and evaluation software that combines the global RGB histogram equalization algorithm, the global HSV histogram equalization algorithm, the restricted contrast adaptive histogram equalization algorithm, the single-scale Retinex algorithm, the multi-scale Retinex algorithm, the Multi-Scale Retinex with Color Restoration, and the dark channel a priori algorithms and their optimization algorithms for image quality improvement and evaluation software. Outdoor images of hazy days, rainy days and snowy days are selected and the best algorithms for different weather conditions are obtained through extensive experimental simulations, software processing and analysis.


Keywords


Defogging Methods; MATLAB; Multi-Algorithms

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References


Lee, DN. The functions of vision[J]. Modes of perceiving and processing information, 1978, 159170.

Nayar S K, Narasimhan S G. Vision in bad weather[C]//Proceedings of the seventh IEEE international conference on computer vision. IEEE, 1999, 2: 820-827.

Tarel JP, Hautiere N. Fast visibility restoration from a single color or gray level image[C]//2009 IEEE 12th international conference on computer vision. IEEE, 2009: 2201-2208.

Xu Y, Wen J, Fei L, et al. Review of video and image defogging algorithms and related studies on image restoration and enhancement[J]. Ieee Access, 2015, 4: 165-188.

Mansson L G. Methods for the evaluation of image quality: a review[J]. Radiation protection dosimetry, 2000, 90(1-2): 89-99.

Wang Z, Bovik AC. Modern image quality assessment[J]. Synthesis Lectures on Image, Video, and Multimedia Processing, 2006, 2(1): 1-156.

Kaur M, Kaur J, Kaur J. Survey of contrast enhancement techniques based on histogram equalization[J]. International Journal of Advanced Computer Science and Applications, 2011, 2(7).

Reza AM. Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement[J]. Journal of VLSI signal processing systems for signal, image and video technology, 2004, 38(1): 35-44.

Rahman Z, Jobson DJ, Woodell GA. Multi-scale retinex for color image enhancement[C]//Proceedings of 3rd IEEE International Conference on Image Processing. IEEE, 1996, 3: 1003-1006.

Funt B, Ciurea F, McCann J. Retinex in matlab[C]//Color and Imaging Conference. Society for Imaging Science and Technology, 2000, 2000(1): 112-121.

He K, Sun J, Tang X. Single image haze removal using dark channel prior[J]. IEEE transactions on pattern analysis and machine intelligence, 2010, 33(12): 2341-235.




DOI: https://doi.org/10.18686/esta.v9i2.237

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