Document Type : Reseach Article
Authors
1 Faculty of Computer Engineering,Faculty of Computer Engineering, Shahrood University of Technology, Shahrood, Iran Shahrood University of Technology, Shahrood, Iran.
2 Faculty of Computer Engineering, Shahrood University of Technology, Shahrood, Iran
3 Department of Engineering and technology, University of Mazandaran, Babolsar, Iran
Abstract
Un-sharp masking method improves the images contrast without requiring any prior knowledge. In this method, a sharper image can be achieved by empowering the high frequency components of the input image. Un-sharp masking has a parameter named gain factor which has a high effect on the enhanced image quality. In this paper, an approach is proposed to adaptively estimate the appropriate value of this parameter in order to effectively enhance an image with local blur, or an image with non-uniform blur. In proposed method, first, the input image is segmented into blur and non-blur regions. Then the gain factor is estimated for each region adaptively. In this approach, the influence of the image blurriness on its gradient information is used to estimate the value for the gain factor. The image quality assessments are applied to evaluate the performance of proposed un-sharp masking method in image enhancement. Experimental results demonstrate that the performance of our proposed method is better than the performance of existing un-sharp masking methods in image enhancement.
Keywords
- [1] Duanmu, F., Wang, G. Teodoro, and J., Kong, “Foveal blur-boosted segmentation of nuclei in histopathology images with shape prior knowledge and probability map constraints”. Bioinformatics, Vol. 37, No. 21, pp. 3905-3913, 2021.
- [2] Berman, T. Treibitz, and S. Avidan, “Single image dehazing using haze-lines,” IEEE Trans. Pattern Anal. Mach. Intell., Vol. 42, No. 3, pp. 720–734, Mar. 2020.
- [3] Ju, C. Ding, Y. J. Guo, and D. Zhang, “IDGCP: Image dehazing based on gamma correction prior,” IEEE Trans. Image Process., Vol. 29, pp. 3104–3118, 2020.
- [4] -J. Liu, S.-J. Horng, J.-S. Lin, and T. Li, “Contrast in haze removal: Configurable contrast enhancement model based on dark channel prior,” IEEE Trans. Image Process., Vol. 28, No. 5, pp. 2212–2227, May 2019.
- [5] Levin, Y. Weiss, F. Durand, and W.T. Freeman, "Understanding blind deconvolution algorithms", IEEE transactions on pattern analysis and machine intelligence, Vol. 33, No. 12, pp.2354-2367, 2011.
- [6] Fan, K. Yang, M. Xia, W. Li, B. Fu, and W. Zhang, "Comparative study on several blind deconvolution algorithms applied to underwater image restoration", Optical review, Vol. 17, No. 3, pp.123-129, 2010.
- [7] S. Almeida, and L.B. Almeida, "Blind and semi-blind deblurring of natural images", IEEE Transactions on Image Processing, Vol. 19, No. 1, pp.36-52, 2010.
- [8] Matsumoto. and T. Furukura, "A new blind deconvolution algorithm based on the probability distribution method", Electrical Engineering in Japan, Vol. 162, No. 1, pp.56-65, 2008.
- [9] Ying, N.T. Ming, and L.B. Keat, "A wavelet based image sharpening algorithm", IEEE International Conference on Computer Science and Software Engineering, Vol. 1, pp. 1053-1056, 2008.
- Zaafouri, M. Sayadi, and F. Fnaiech, "A developed unsharp masking method for images contrast enhancement", IEEE International Multi-Conference on Systems, Signals and Devices (SSD), pp. 1-6, 2011.
- L.D.A. Mai, M.T.T. Nguyen, and N.M. Kwok, "A modified unsharp masking method using particle swarm optimization", IEEE International Congress on Image and Signal Processing (CISP), Vol. 2, pp. 646-650, 2011.
- Kwok, and H. Shi, "Design of unsharp masking filter kernel and gain using particle swarm optimization", IEEE International Congress on Image and Signal Processing (CISP), pp. 217-222, 2014.
- Mortezaie, H. Hassanpour, and S. Asadi Amiri, "Image Enhancement Using an Adaptive Un-sharp Masking Method Considering the Gradient Variation", International Journal of Engineering (IJE), Vol. 30, No. 8, pp. 1118-1125, 2017.
- Ngo, S. Lee and B. Kang, "Nonlinear Unsharp Masking Algorithm", 2020 International Conference on Electronics, Information, and Communication (ICEIC), Barcelona, Spain, pp. 1-6, 2020, doi: 10.1109/ICEIC49074.2020.9051376.
- Polesel, G. Ramponi, and V.J. Mathews, "Image enhancement via adaptive unsharp masking", IEEE transactions on image processing, Vol. 9, No. 3, pp.505-510, 2000.
- Jane, and H.G. Ilk, "Priority and significance analysis of selecting threshold values in adaptive unsharp masking for infrared images", IEEE International Conference on Microwave Techniques (COMITE), pp. 9-12, 2010.
- Chitwong, S. Phahonyothing, P. Nilas, and F. Cheevasuvit, "Contrast enhancement of satellite image based on adaptive unsharp masking using wavelet transform", In ASPRS 2006 Annual Conference, Reno, Nevada.
- Mortezaie, H. Hassanpour, and S. Asadi Amiri, "An adaptive block based un-sharp masking for image quality enhancement", Multimedia Tools and Applications, Vol. 78, No. 16, pp.23521-23534, 2019.
- C.F. Lin, C.Y. Wong, G. Jiang, M.A. Rahman, T.R. Ren, N. Kwok, H. Shi, Y.H. Yu, and T. Wu, "Intensity and edge based adaptive unsharp masking filter for color image enhancement", Optik-International Journal for Light and Electron Optics, Vol. 127, No. 1, pp.407-414, 2016.
- Askari Javaran, H. Hassanpour, and V. Abolghasemi, "Automatic estimation and segmentation of partial blur in natural images", The Visual Computer: International Journal of Computer Graphics, Vol. 33, No. 2, pp.151-161, 2017.
- Yang, and T. Jiang, "Pixon-based image segmentation with Markov random fields", IEEE Transactions on Image Processing, Vol. 12, No. 12, pp.1552-1559, 2003.
- Asadi Amiri, H. Hassanpour, and O.R. Marouzi, "No-reference image quality assessment based on localized discrete cosine transform for JPEG compressed images", Multimedia Tools and Applications, Vol. 77, No. 1, pp.787-803, 2017.
- Varga, D., “No-reference image quality assessment with global statistical features”. Journal of Imaging, 7, No. 2, p.29, 2021.
- Ding, K., Ma, K., Wang, S. and Simoncelli, E.P., “Comparison of full-reference image quality models for optimization of image processing systems”.International Journal of Computer Vision,129, No. 4, pp.1258-1281, 2021.
- K. Pratt, Digital Image Processing, Wiley, New York, 1978.
- Goel, N., “Modified decision based two-phase unsymmetrical trimmed/winsorized mean filter for removal of very high density salt and pepper noise from images and videos”.Multimedia Tools and Applications, pp.1-27, 2022.
- Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli, "Image quality assessment: from error visibility to structural similarity", IEEE transactions on image processing, Vol. 13, No. 4, pp.600-612, 2004.
- Li, and A.C. Bovik, "Content-partitioned structural similarity index for image quality assessment", Signal Processing: Image Communication, Vol. 25, No. 7, pp.517-526, 2010.
- Zhang, F. Zou, and J. Zheng, "The Linear Transformation Image Enhancement Algorithm Based on HSV Color Space", In Advances in Intelligent Information Hiding and Multimedia Signal Processing: Proceeding of the Twelfth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 19-27, 2017.
- Martin, C. Fowlkes, D. Tal, and J. Malik, "A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics", IEEE International Conference on Computer Vision, (ICCV), Vol. 2, pp. 416-423, 2001.
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