Adaptive-neighborhood histogram equalization of color images

Histogram equalization (HE) is one of the simplest and most effective techniques for enhancing gray-level images. For color images, HE becomes a more difficult task, due to the vectorial nature of the data. We propose a new method for color image enhancement that uses two hierarchical levels of HE: global and local. In order to preserve the hue, equalization is only applied to intensities. For each pixel (called the ‘‘seed’’ when being processed) a variable-sized, variable-shaped neighborhood is determined to contain pixels that are ‘‘similar’’ to the seed. Then, the histogram of the region is stretched to a range that is computed with respect to the statistical parameters of the region (mean and variance) and to the global HE function (of intensities), and only the seed pixel is given a new intensity value. We applied the proposed color HE method to various images and observed the results to be subjectively ‘‘pleasant to the human eye,’’ with emphasized details, preserved colors, and with the histogram of intensities close to the ideal uniform one. The results compared favorably with those of three other methods (histogram explosion, histogram decimation, and three-dimensional histogram equalization) in terms of subjective visual quality.

[1]  Jeffrey J. Rodríguez,et al.  A multivariate contrast enhancement technique for multispectral images , 1995, IEEE Trans. Geosci. Remote. Sens..

[2]  Titus Zaharia,et al.  Adaptive neighborhood filters for color image filtering , 1998, Electronic Imaging.

[3]  Rangaraj M. Rangayyan,et al.  Histogram equalization of color images using the adaptive neighborhood approach , 1999, Electronic Imaging.

[4]  Rangaraj M. Rangayyan,et al.  Adaptive neighborhood mean and median image filtering , 1994, J. Electronic Imaging.

[5]  Arthur R. Weeks,et al.  Histogram specification of 24-bit color images in the color difference (C-Y) color space , 1999, J. Electronic Imaging.

[6]  Christopher C. Yang,et al.  High-Resolution Histogram Modification of Color Images , 1995, CVGIP Graph. Model. Image Process..

[7]  I. M. Bockstein.  Color equalization method and its application to color image processing , 1986 .

[8]  Thomas S. Huang,et al.  Image processing , 1971 .

[9]  Giordano B. Beretta.  Dynamic exposure control in color scanners , 1997, Electronic Imaging.

[10]  R M Rangayyan,et al.  Image restoration by adaptive-neighborhood noise subtraction. , 1994, Applied optics.

[11]  Rangaraj M. Rangayyan,et al.  Iterative method for blind deconvolution , 1994, J. Electronic Imaging.

[12]  R M Rangayyan,et al.  Adaptive-neighborhood filtering of images corrupted by signal-dependent noise. , 1998, Applied optics.

[13]  Rangaraj M. Rangayyan,et al.  Filtering multiplicative noise in images using adaptive region-based statistics , 1998, J. Electronic Imaging.

[14]  P. A. Mlsna,et al.  A recursive technique for 3-D histogram enhancement of color images , 1996, Proceeding of Southwest Symposium on Image Analysis and Interpretation.

[15]  Panos Trahanias,et al.  Color image enhancement through 3-D histogram equalization , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol. III. Conference C: Image, Speech and Signal Analysis,.

[16]  Rangaraj M. Rangayyan,et al.  Filtering noise in color images using adaptive-neighborhood statistics , 2000, J. Electronic Imaging.

[17]  Rangaraj M. Rangayyan,et al.  Region-based contrast enhancement of mammograms , 1992, IEEE Trans. Medical Imaging.

[18]  Ioannis Pitas,et al.  Multichannel techniques in color image enhancement and modeling , 1996, IEEE Trans. Image Process..

[19]  Rangaraj M. Rangayyan,et al.  Adaptive-neighborhood histogram equalization for image enhancement , 1992, CVGIP Graph. Model. Image Process..

[20]  Qiang Zhang,et al.  3-D histogram modification of color images , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[21]  Anil K. Jain.  Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.