An improved image sharpness assessment method based on contrast sensitivity

Li Zhang; Yan Tian; Yili Yin

doi:10.1117/12.2203096

8 October 2015 An improved image sharpness assessment method based on contrast sensitivity

Li Zhang, Yan Tian, Yili Yin

Proceedings Volume 9675, AOPC 2015: Image Processing and Analysis; 967535 (2015) https://doi.org/10.1117/12.2203096
Event: Applied Optics and Photonics China (AOPC2015), 2015, Beijing, China

Abstract

An image sharpness assessment method based on the property of Contrast Sensitivity Function (CSF) was proposed to realize the sharpness assessment of unfocused image. Firstly, image was performed the two-dimensional Discrete Fourier Transform (DFT), and intermediate frequency coefficients and high frequency coefficients are divided into two parts respectively. Secondly the four parts were performed the inverse Discrete Fourier Transform (IDFT) to obtain subimages. Thirdly, using Range Function evaluates the four sub-image sharpness value. Finally, the image sharpness is obtained through the weighted sum of the sub-image sharpness value. In order to comply with the CSF characteristics, weighting factor is setting based on the Contrast Sensitivity Function. The new algorithm and four typical evaluation algorithm: Fourier, Range , Variance and Wavelet are evaluated based on the six quantitative evaluation index, which include the width of steep part of focusing curve, the ration of sharpness, the steepness, the variance of float part of focusing curve, the factor of local extreme and the sensitivity. On the other hand, the effect of noise, and image content on algorithm is analyzed in this paper. The experiment results show that the new algorithm has better performance of sensitivity, anti-nose than the four typical evaluation algorithms. The evaluation results are consistent with human visual characteristics.

Citation Download Citation

Li Zhang, Yan Tian, and Yili Yin "An improved image sharpness assessment method based on contrast sensitivity", Proc. SPIE 9675, AOPC 2015: Image Processing and Analysis, 967535 (8 October 2015); https://doi.org/10.1117/12.2203096

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