Histogram equalization

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

Color image segmentation using acceptable histogram segmentation

Abstract: In this paper, a new method for the segmentation of color images is presented. This method searches for an acceptable segmentation of 1D-histograms, according to a “monotone” hypothesis. The algorithm uses recurrence to localize all the modes in the histogram. The algorithm is applied on the hue, saturation and intensity histograms of the image. As a result, an optimal and accurately segmented image is obtained. In contrast to previous state of the art methods uses exclusively the image color histogram to perform segmentation and no spatial information at all.

Extraction of a color palette model from an image of a document

Abstract: A system and method are provided for determining a color palette model from an image of a document. Pixel values of the image of the document are clustered to provide image clusters. Color layers of the image are determined, each color layer corresponding to an image cluster. Aspects of the color palette model can be determined using the color layers. Aspects of the color palette model include a foreground-background color pair for a content block in the document and a background-area color of the document.

Exposure-Based Energy Curve Equalization for Enhancement of Contrast Distorted Images

Abstract: This paper presents a novel image contrast enhancement technique that uses exposure-based energy curve equalization (ECE) with a plateau limit. In a primary deviation from the current histogram equalization process for contrast enhancement, the proposed approach uses an energy curve for the same. The energy curve is computed based on the modified Hopfield neural network architecture, which contains spatial context information. The calculated energy curve is clipped with a plateau limit computed as the average of the energy curve. The exposure threshold is computed and used to divide the clipped energy curve. The two resulting energy curves are equalized independently, and the final enhanced image is generated by integrating the images achieved by transforming the equalized energy curves. The performance of the proposed method is evaluated on a variety of low contrast images. The subjective and objective evaluations of the proposed method are compared with the various histogram equalization (HE) based methods and other state-of-the-art methods to exemplify the effectiveness.

A Grey-Level Image Embedding its Color Palette

Abstract: In this paper, we propose a method to embed the color information of an image in its corresponding grey-level image. The objective of this work is to allow free access to the grey-level image and give color image access to secret key owners. This method is made of two steps which are the color image decomposition (in a grey-level image and its associated color information) and the data-hiding. The main contribution of this paper is the energetic function proposed to model the decomposition of the color image. The optimization of the proposed energetic function leads to the obtention of an index image and a color palette. The good properties of that decomposition are an index image which is similar to the luminance of the color image and a color palette which is well suit for the data-hiding. The obtained results confirm the model quality.

Acoustic image enhancement using Gaussian and laplacian pyramid --- a multiresolution based technique

Abstract: Acoustic images captured by side scan sonar are normally affected by speckle noise for which the enhancement is required in different domain. The underwater acoustic images obtained using sound as a source, basically contain seafloor, sediments, living and non-living resources. The Multiresolution based image enhancement techniques nowadays play a vital role in improving the quality of the low resolution image with repeated patterns. Image pyramid is the representation of an image at various scales. In this work, a three level Gaussian and Laplacian pyramids are constructed to represent the image in different resolution. The multiscale representation requires different filters at different scales. The contrast of each image in Gaussian and Laplacian pyramids are improved by applying both histogram equalization and unsharp masking method. The sharpened images are used to reconstruct the enhanced image. The performance measure, peak signal to noise ratio proves that the unsharp masking method applied to difference images of Laplacian pyramid outperforms the other image enhancement methods.