A New Approach to Image Enhancement Based on the Use of Raw Moments for Subranges of Brightness

The problem of improving the quality of complex low-contrast images in automatic mode with an acceptable level of computational costs is considered in this paper. The task of increasing the contrast for complex low-contrast images with a wide dynamic range and multi-modal distribution of brightness is considered. The purpose of this work is to improve the efficiency of increasing the overall contrast for complex images with a wide dynamic range and multi-modal distribution of brightness. A new approach to increase the contrast of complex low-contrast image by its adaptive non-linear contrast stretching is proposed based on the measuring of raw moments for subranges of image brightness. The proposed approach is based on measuring the ratios between the values of the raw moments for different subranges of brightness. A new technique of contrast enhancement for complex monochrome images based on measuring the mean values for subranges of image brightness is also proposed. The research of the various known and proposed techniques of image contrast enhancement in the automatic mode was carried out using the no-reference metrics of overall image contrast. The proposed technique provides an efficient redistribution of the contrast of objects in the image regardless of their size and enables to effectively increase the overall contrast of the image in automatic mode with an acceptable level of computational costs.

[1]  Zhou Wang,et al.  Modern Image Quality Assessment , 2006, Modern Image Quality Assessment.

[2]  David J. Ketcham Real-Time Image Enhancement Techniques , 1976, Other Conferences.

[3]  Manpreet Kaur,et al.  Survey of Contrast Enhancement Techniques based on Histogram Equalization , 2011 .

[4]  Yeong-Taeg Kim,et al.  Contrast enhancement using brightness preserving bi-histogram equalization , 1997 .

[5]  Robert A. Hummel,et al.  Image Enhancement by Histogram transformation , 1975 .

[6]  William K. Pratt,et al.  Digital Image Processing: PIKS Inside , 2001 .

[7]  Abd. Rahman Ramli,et al.  Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation , 2003, IEEE Trans. Consumer Electron..

[8]  Qian Chen,et al.  Image enhancement based on equal area dualistic sub-image histogram equalization method , 1999, IEEE Trans. Consumer Electron..

[9]  Sanjay S. Gharde,et al.  REVIEW OF VARIOUS IMAGE CONTRAST ENHANCEMENTTECHNIQUES , 2013 .

[10]  H. N. Hazlyna,et al.  Image Enhancement Techniques Using Local, Global, Bright, Dark and Partial Contrast Stretching For Acute Leukemia Images , 2009 .

[11]  D. Venkat Reddy,et al.  A Comparative Analysis of Histogram Equalization based Techniques for Contrast Enhancement and Brightness Preserving , 2013 .

[12]  Werner Frei,et al.  Image Enhancement by Histogram Hyperbolization , 1977 .

[13]  E. Peli Contrast in complex images. , 1990, Journal of the Optical Society of America. A, Optics and image science.

[14]  Y. Romanyshyn,et al.  No-reference metric of generalized contrast for complex images , 2017, 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON).

[15]  Norio Akamatsu,et al.  A New Approach for Contrast Enhancement Using Sigmoid Function , 2004, Int. Arab J. Inf. Technol..

[16]  Sergei Yelmanov,et al.  A method for rapid quantitative assessment of incomplete integral contrast for complex images , 2018, 2018 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET).

[17]  Dae San Kim,et al.  Home network message specification for white goods and its applications , 2002, IEEE Trans. Consumer Electron..