Histogram equalization

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

Image enhancement for radiography inspection

Abstract: The x-ray radiographic testing method is often used for detecting defects as a non-destructive testing method (NDT). In many cases, NDT is used for aircraft components, welds, etc. Hence, the backgrounds are always more complex than a piece of steel. Radiographic images are low contrast, dark and high noise image. It is difficult to detect defects directly. So, image enhancement is a significant part of automated radiography inspection system. Histogram equalization and median filter are the most frequently used techniques to enhance the radiographic images. In this paper, the adaptive histogram equalization and contrast limited histogram equalization are compared with histogram equalization. The adaptive wavelet thresholding is compared with median filter. Through comparative analysis, the contrast limited histogram equalization and adaptive wavelet thresholding can enhance perception of defects better.

Image process apparatus and method

Abstract: There is provided an image processing method for generating a color image by using a color palette table, comprising the steps of a restriction step of restricting colors capable of being registered in the palette table, on the basis of gamut data of an image output means for outputting the color image, a formation step of forming the color palette table on the basis of color restriction obtained in the restriction step, and a generation step of generating the color image by using the colors registered in the color palette table, whereby excellent color matching between the color on a monitor and a color on a printer can be obtained.

A Method of Color Histogram Creation for Image Retrieval

Abstract: The traditional method of color histogram creation is toequally subdivide a color space (e.g. RGB, HSI) into acertain number of bins and then count the number of pix-els each bin contains. This strategy results in a quite largenumber of bins with trivial color differences betweenadjacent bins. Consequently small changes in the scene(e.g. changes in the illumination conditions, presence ofnoise) may cause important modifications of the histo-gram. We proposed a new method of color histogram cre-ation based exclusively on the hue component in thechromatic image region and on the intensity componentin the achromatic image region. The color appearance ofthe image is described using a relatively small number ofbins. The proposed method of histogram creation hasbeen evaluated based on the performances achieved inretrieving similar images in a heterogeneous image col-lection. The experimental results reveal that the proposedmethod is less sensitive to small changes in the sceneachieving higher retrieval performances than the tradi-tional method of histogram creation.1. INTRODUCTIONThe most popular technique for image retrieval in a heter-ogeneous collection of images is the comparison ofimages based on their histograms. The histogramdescribes the gray-level or color distribution for a givenimage. It is a global feature which can be used to performa fast but no so reliable indexing process. The histogramfeature can be used as a preliminary step for databaseindexing in order to reduce the number of candidateimages for the next steps which could use other features(e.g. shape, texture, orientation) to compare the databaseimages with a given query image. The major advantageoffered by the histogram feature consists in its small sen-sitivity to scale, rotation and translation [1]. An appropri-ate color space, a color quantization scheme, a histogramrepresentation, and a similarity metric are the main ingre-dients required for the design of a histogram basedretrieval system [2]. The RGB color space is inappropri-ate for image retrieval due to the fact that it is not relatedwith the way humans perceive colors. Other color spaceslike opponent color space [1], HSI or YIQ are generallyused for retrieval proposes [2], [3]. The Lu*v* space isalso used because it yields a perceptually uniform spac-ing of colors [4].Once a certain color space is subdivided in a number ofbins, the histogram is created by simply counting thenumber of pixels each bin contains. This strategy usuallyresults in a very large number of bins, and hence thecolors represented by adjacent bins would reveal onlytrivial differences. Consequently small changes in thescene (e.g., change in the illumination conditions) or thepresence of noise usually determine large number of pix-els to drift from one bin to another. As a result twoimages which are quite similar one to each other mayhave very different histogram representations.In our method a relatively small number of bins is used inorder to describe the most prominent colors which maybe perceived in the image. The histogram is created basedon the hue and intensity components. The two compo-nents are weighted according with their relevance in dif-ferent image regions based on the value of standarddeviation of the RGB tristimuli.The paper is organized as follows. The proposed methodof histogram creation is described in Section 2. Someexperimental results and comparisons are shown in Sec-tion 3, and some concluding remarks are then presentedin Section 4.2. THE PROPOSED METHODThe hue (H) component is the most suitable one to use inorder to describe the color content of a digital image. Itcontains most of the color information and hence it isalmost constant regardless of the changes in the illumina-tion conditions (e.g., shadows which usually occlude theobjects in a natural image) [5]. However, natural imagesoften contain achromatic regions where the hue compo-

Bleeding detection in wireless capsule endoscopy based on color features from histogram probability

Abstract: This paper presents a novel technique for detecting bleeding regions in capsule endoscopy images. The proposed algorithm extracts color features from image-regions by calculating mean, standard deviation, skew and energy from the first order histogram of the RGB planes separately. Through the use of RGB color space, three times more number of features can be obtained than while using a grayscale image. Such color features have been used in content based retrieval system in pathology images. However, in spite of simplicity and ease of calculation, these features have not yet been studied in the classification of bleeding and non-bleeding regions in capsule endoscopic images. This paper studies the feasibility of using these features by assessing all possible feature subsets through the use of classification accuracy. The proposed algorithm could obtain classification accuracy up to 89%.

A Multidimensional Histogram Equalization by Fitting an Isotropic Gaussian Mixture to a Uniform Distribution

Abstract: In this paper, a novel method to extend the grayscale histogram equalization (GHE) for color images in a multi-dimension is proposed. Unlike most current techniques, the proposed method can generate a uniform histogram, thus minimizing the disparity between the histogram and uniform distribution. A histogram of any dimension is regarded as a mixture of isotropic Gaussians. This method is a natural extension of the GHE to a multi-dimension. An efficient algorithm for the histogram equalization is provided. The results show that this approach is valid, and a psycho-visual study on a target distribution will improve the practical use of the proposed method.