Histogram equalization

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

Image Retrieval Based on Color-Spatial Feature

Abstract: The color histogram based image retrieval method is simple and efficient but losing the spatial distribution information of the color. In this paper, a color-space feature based image retrieval method is presented. The content of one image is looked as the aggregation of some main objects, which can be obtained by image segmentation. The total similarity between two images is computed according to these main objects?color, location and shape features and then retrieval there images. The experimental results show that the method is more accurate and efficient in retrieving the user interested images than color histogram method when there are obvious objects in the image.

Method of adjusting image pixel values within an image sequence by interpolation of target cumulative histograms for images to be adjusted

Abstract: A method of interpolating a reference cumulative histogram for a source image located within an image sequence, the reference cumulative histogram to be used to adjust image pixel code values of the source image to remove unwanted variations in color or tone, e.g. "flicker", while preserving an intended variation, e.g. "fade to black" in the color or tone occurring during the course of the image sequence. The method employs conversion of cumulative histograms derived from reference image frames at opposite ends of the image sequence into inverse cumulative histograms and the weighted averaging of these inverse cumulative histogram values based on the time location of the source image frame in the sequence to derive an interpolated inverse cumulative histogram which is then converted into the reference cumulative histogram used for adjusting pixel code values, for example by histogram matching, to remove the undesired variation in color or tone without loss of the intentional variation in color or tone.

Image processing apparatus and method for magnifying dynamic range

Abstract: An apparatus and method for magnifying a dynamic range of an image while improving a contrast of an image are provided. A histogram calculator receives a digital image signal and calculates histograms depending on the illuminance distribution of an image. A histogram accumulator receives a histogram output by the histogram calculator, obtains an accumulative density function by integrating the input histogram up to each illuminance level and normalizes the accumulative density function, and generates an initial conversion function. A bias calculator determines a threshold value for dividing into a low illuminance level and a high illuminance level on the basis of a histogram output by the histogram calculator, and obtains a histogram compensation function for applying a negative bias to a histogram around the threshold value. A converter converts a digital image signal input with a predetermined delay time, according to an image conversion function obtained by applying the histogram compensation function to the initial conversion function. A digital-to-analog (D/A) converter converts a digital image signal converted by the converter into an analog image signal. Accordingly, the entire contrast of an image is improved, and simultaneously, the dynamic range thereof is magnified.

A method to improve the image enhancement result based on image fusion

Abstract: Image enhancement can improve the perception of information. An image taken from a real scene can be divided into several regions according to the need for enhancement. One particular enhancement method improves some regions and actually deteriorates the other regions which have no need for such enhancement or any enhancement at all. This paper proposes a method to improve the enhancement result with image fusion method with evaluation on sharpness. Several different evaluation methods and fusion policies are discussed and compared. The experiment results show that the fusion improves the enhancement results.

Reversible Data Hiding with Image Contrast Enhancement Based on Two-Dimensional Histogram Modification

Abstract: Recently, reversible image data hiding with contrast enhancement (CE) has been proposed so that a contrast-changed image can be converted to its original version when needed. Several reversible image CE methods have been proposed by adopting the technique of reversible data hiding (RDH) to embed the recovery information into the contrast-enhanced images. In these methods, the 1D histogram is calculated and then modified to achieve the effects of histogram equalization (HE). Meanwhile, the 2D histogram has been used in image CE and demonstrated remarkable advantages in improving the image quality. In this paper, the method based on the 2D histogram modification is proposed to perform image CE and RDH simultaneously. In particular, a preprocessing strategy is developed to merge the adjacent bins in 2D histogram to prevent the overflow and underflow of the pixel values due to HE. The changes made in preprocessing is minimized by choosing the lowest bins for merging, while the CE effects can be achieved by expanding the highest bins for data embedding. The experimental results on two sets of test images have clearly demonstrated the efficacy of the proposed method for reversible image CE. The evaluation results are given to show its superior performances in CE and image quality preservation by comparing with the state-of-the-art approaches.