Transform Coefficient Histogram-Based Image Enhancement Algorithms Using Contrast Entropy

Abstract

Many applications of histograms for the purposes of image processing are well known. However, applying this process to the transform domain by way of a transform coefficient histogram has not yet been fully explored. This paper proposes three methods of image enhancement: a) logarithmic transform histogram matching, b) logarithmic transform histogram shifting, and c) logarithmic transform histogram shaping using Gaussian distributions. They are based on the properties of the logarithmic transform domain histogram and histogram equalization. The presented algorithms use the fact that the relationship between stimulus and perception is logarithmic and afford a marriage between enhancement qualities and computational efficiency. A human visual system-based quantitative measurement of image contrast improvement is also defined. This helps choose the best parameters and transform for each enhancement. A number of experimental results are presented to illustrate the performance of the proposed algorithms.

Acceleration of histogram‐based contrast enhancement via selective downsampling

“…Local CE improves contrast by altering pixels in terms of local properties, and typically operates in the image transform domains, such as the discrete cosine transform (DCT) [4] and the discrete wavelet transform (DWT) [5]. Local CE can also be enforced by adaptively applying the global CE to local image regions [3].…”

“…The accelerated HE algorithm is proposed as follows, Comparing with the baseline HE algorithm [1], the integrated acceleration measures are included in the steps (1), (2), (4). Specifically, in the step (1), spatial image downsampling decreases the number of counted pixels, which can accelerate the generation of histogram.…”

A Comprehensive Reference Source for the Researchers Involved in Image Enhancement Field – A Review

“…al. [9] proposed logarithmic transform domain histogram and histogram equalization for image enhancement. This paper helps choose the best parameters and transform for each enhancement.…”

“…Year Operating Domain Model M. Yasmin [6] 2012 Transform domain, spatial domain Noise reduction, resolution, segmentation, noise suppression J. K. Hasikin [7] 2012 Spatial domain Fuzzy-based contrast modification R. Arun [8] 2011 Spatial domain Alpha rooting based hybrid Procedure S. S. Agaian [9] 2007 Transform domain, spatial domain Transform coefficient histogram-based image enhancement algorithms M. A. Wadud [10] 2007 Spatial domain Conventional histogram equalization L. Xiaoying [11] 2011 Transform domain, spatial domain Image fusion method evaluation on sharpness J. Mohan [12] 2014 Filtering, transform domain, statistical approach Noise reduction L. S. Chow [13] 2016 Spatial domain Subjective assessments, subjective assessments K. Binaee [14] 2014 Filtering Speckle reduction S. S. Suganthi [15] 2014 Filtering Edge enhancement for segmentation S. Anand [16] 2013 Transform domain Directionlet transform (DT)…”

An Efficient Method for Contrast Enhancement in Still Images using Histogram Modification Framework

“…In order to overcome the limitations of HE, several brightness preserving methods have been proposed (Kim et al, 2001;Chen and Ramli, 2003a;Sun et al, 2005;Chen et al, 2006;Wang and Ward, 2007;Sengee and Choi, 2008;Wang and Ye, 2005;Agaian et al, 2007). One of the popular brightness preserving methods is the mean Brightness preserving BiHistogram Equalization (BBHE) introduced by Kim (Chen and Ramli, 2003b).…”