Histogram equalization

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

Improved local histogram equalization with gradient-based weighting process for edge preservation

Abstract: This paper presents a novel local histogram equalization by combining the transformation functions of the non-overlapped sub-images based on the gradient information for edge preservation and better visualization. To ameliorate the problems of the over- and under-enhancement produced by conventional local histogram equalization, the bilateral Bezier curve-based histogram modification strategy is first employed to modify the significant and insufficient changes of each cumulative distribution in each sub-image. Yet, the gradient information has not been considered, and the cumulative distribution of some enhanced sub-images are still significant or insufficient because of the over- and under-enhancement, respectively. Therefore, the key insight of the proposed method is that the transformation functions of the partitioned sub-images will be weighed and combined based on the proportion of gradients to preserve the image texture. In addition, the input image is separated into the non-overlapped sub-images for reducing the time complexity. Based on the eight representative test images and mean opinion score, the experimental results demonstrate that the proposed method is quite competitive with four state-of-the-art histogram equalization methods in the literature. Furthermore, according to the subjective evaluation, it is observed that the proposed method can also apply to the practical applications and achieve good visual quality.

Image contrast enhancement based on 2D Teager-Kaiser operator

Abstract: This paper describes a new method based on the 2D Teager- Kaiser Energy Operator (2DTKEO) for image contrast enhancement. The 2DTKEO reflects better the local activity than the amplitude of a classical edges detection operator. This quadratic filter is used to enhance high frequency information which is then combined with image gray values to estimate the edge strength value used in the enhancement process. This value is the average of the gray values by the energy activity at each pixel. Different examples of images are provided to demonstrate the Performance of the proposed method is demonstrated on synthetic and real images and the results compared to histogram equalization and to an edge- based contrast method.

A robust multisource image automatic registration system based on the SIFT descriptor

Abstract: Image registration is an essential step in many remote-sensing RS applications. This article presents a study of a multisource image automatic registration system MIARS based on the scale-invariant feature transform SIFT, which has been demonstrated to be the most robust local invariant feature descriptor for automatically registering various RS images. The SIFT descriptor has two shortcomings: it is unsuitable for extremely large images and has an irregular distribution of feature points. Therefore, three steps are proposed for the MIARS: image division, histogram equalization and the elimination of false point matches by a subregion least squares iteration. Image division makes it possible to use the SIFT descriptor to extract control points from an extremely large RS image. Histogram equalization in prematching improves the contrast sensitivity of RS images. The subregion least squares iteration refines the registration accuracy. Images from multisensor systems, including Quickbird, IRS-P6, Landsat/TM, HJ-CCD, HJ-IRS, light detection and ranging LiDAR intensity images and aerial data, were selected to test the reliability of the MIARS. The results indicated that better registration accuracy was achieved, which will be very helpful in the future development of a registration model.

From image processing to computational neuroscience: a neural model based on histogram equalization

Abstract: There are many ways in which the human visual system works to reduce the inherent redundancy of the visual information in natural scenes, coding it in an efficient way. The non-linear response curves of photoreceptors and the spatial organization of the receptive fields of visual neurons both work toward this goal of efficient coding. A related, very important aspect is that of the existence of post-retinal mechanisms for contrast enhancement that compensate for the blurring produced in early stages of the visual process. And alongside mechanisms for coding and wiring efficiency, there is neural activity in the human visual cortex that correlates with the perceptual phenomenon of lightness induction. In this paper we propose a neural model that is derived from an image processing technique for histogram equalization, and that is able to deal with all the aspects just mentioned: this new model is able to predict lightness induction phenomena, and improves the efficiency of the representation by flattening both the histogram and the power spectrum of the image signal.

Weighted average multi segment histogram equalization for brightness preserving contrast enhancement

Abstract: Histogram equalization (HE) is one of the most effective method for contrast enhancement, but it fails to preserve the mean brightness of images. To overcome such drawback, several Bi- and Multi-histogram equalization methods have been proposed. Among them, Bi-HE methods may preserve the brightness, but they introduce some undesirable artifacts in the processed image. On the other hand, Multi-HE methods may not introduce undesirable artifacts in image but at the cost of either the brightness or its contrast. In this paper, we propose a weighted average multi segment HE method using Gaussian filter for contrast enhancement of natural images while preserving mean brightness. It also reduces noise present in the images. The proposed method first smooths the global histogram and decomposes it into multiple segments via optimal thresholds, and then HE is applied to each segment independently. Simulation results for several test images show that the proposed method enhances the contrast while preserving mean brightness.