Histogram equalization

About: Histogram equalization is a research topic. Over the lifetime, 5755 publications have been published within this topic receiving 89313 citations.

Optimized Perceptual Tone Mapping for Contrast Enhancement of Images

Abstract: Existing contrast enhancement techniques, such as histogram equalization (HE) and optimal contrast-tone mapping (OCTM), construct a pixel mapping function based on probability. However, they often allocate a large dynamic range to smooth areas, thus preventing the allocation of dynamic range resources in some regions that people are more interested in. To deal with this problem, we propose optimized perceptual tone mapping (OPTM), which performs contrast enhancement of images considering human visual attention. First, we construct a saliency histogram based on human visual attention to quantitatively measure the degree of visual attention instead of a probability histogram. Then, we perform contrast enhancement of images subject to constraints, such as the maximum tone distortion allowed. Finally, we adjust a pixel mapping function based on a just-noticeable-distortion model to prevent overenhancement. The experimental results demonstrate that OPTM produces visually pleasing results without overenhancement in large smooth regions and achieves a significant performance improvement in contrast enhancement.

Histogram refinement for texture descriptor based image retrieval

Abstract: Texture descriptors such as local binary patterns (LBP) have been successfully employed for feature extraction in image retrieval algorithms because of their high discriminating ability and computational efficiency. In this paper, we propose histogram feature refinement methods for enhancing performance of texture descriptor based content-based image retrieval (CBIR) systems. In the proposed approach for histogram refinement, each pixel in the query and database images is classified into one of the two categories based on the analysis of pixel values in its neighborhood. Local patterns corresponding to two sets of pixels are used to generate two histogram features for each image, effectively resulting in splitting of the original global histogram of texture descriptors into two based on the category of each pixel. Resulting histograms are then concatenated to form a single histogram feature. This study also explores three hybrid frameworks for histogram refinement in CBIR systems. Comparison of histogram features corresponding to query and database images are performed using the relative l1 distance metric. Performance evaluation on three publicly available benchmark image databases namely, GHIM 10000, COREL 1000 database, and Brodatz texture database shows that performances of existing texture descriptor based approaches improve considerably when the proposed histogram feature refinement is incorporated. Specifically, the average precision rate is improved by 6.02%, 5.69%, 4.79%, and 4.21% for LBP, local derivative pattern (LDP), local ternary pattern (LTP), and local tetra pattern (LTrP) descriptors, respectively on GHIM 10000 database. The proposed histogram refinement approaches also provide performance improvement for other texture descriptors considered in this study. A general framework for histogram refinement of texture descriptors.Improves retrieval performance of texture descriptor based CBIR systems.Performance improvement in CBIR validated using 9 texture descriptors.Marginal increase in average image retrieval time with 1.2s in the worst case.

Edge color histogram for image retrieval

Abstract: The classical color histogram for image indexing does not take into account the shape information of an image. A color histogram method with edge information is studied. Color distributions are found for the pixels of three types of edges (two directional edges and one non-directional edge) and three distance measures are computed on the basis of the color distribution of each edge type. The proposed similarity measure obtained by combining these three distance measures could reduce the false match rate in comparison with using only single (non-directional) edge types. Simulation results show an improvement in indexing quality as compared to that of the traditional color histogram and edge histogram.

Bi-histogram equalization using two plateau limits

Abstract: Histogram equalization is an effective method for contrast enhancement on images, but it suffers from some problems such as the tendency to change the mean brightness, loss of information and the introduction of saturation levels which causes an unnatural appearance in the resulting image. Due to the aforementioned problems, a variety of histogram equalization methods have been developed in order to preserve the image brightness, thus avoiding saturation levels that cause loss of information. In this paper, the bi-histogram equalization using two plateau limits (BHE2PL) for histogram equalization is proposed. BHE2PL divides the global histogram into two sub-histograms; then, each sub-histogram is modified by two plateau limits in order to avoid over-enhancement of the image. Experimental results indicate that the BHE2PL method exhibits a better mean brightness preservation compared to methods found in the state of the art; in addition to also presenting a reasonable computation time.

Digital color image processing method and apparatus employing three color reproduction functions for adjusting both tone scale and color balance

Abstract: A digital color image processing method and apparatus is characterized by producing three color reproduction functions by normalizing samples of color values from three color components of a digital image. The color reproduction functions are applied to the respective color components of the image to produce dimensionless Z values. A contrast adjusting constant is computed and the dimensionless Z values are multiplied by the constant to adjust the contrast of the processed image. Finally a constant representing the mean density for each of the color components for a particular output medium is added to the contrast adjusted Z values to produce the processed digital image.