Bi-histogram equalization using modified histogram bins

The shifting of image mean brightness and the domination of high-frequency bins during histogram equalization (HE) often result in the deteriorating quality of enhanced images and a considerable amount of information loss. This study proposes a novel approach based on bi-histogram equalization to improve its abilities in preserving information entropy and mean brightness. The proposed technique, named Bi-histogram Equalization using Modified Histogram Bins (BHEMHB), segments the input histogram based on the median brightness of an image and alters the histogram bins before HE is applied. Histogram segmentation enables mean brightness preservation, whereas the modification of histogram bins restricts the enhancement rate, thus minimizing the domination effects of high-frequency histogram bins. Simulation results show that BHEMHB significantly outperforms its peers in preserving the details and mean brightness of an image. The output image is visually pleasant with a natural appearance.     

Image contrast enhancement using fuzzy clustering with adaptive cluster parameter and sub-histogram equalization

Histogram equalization is an effective technique to boost image quality and contrast enhancement. However, in some cases the increase in image contrast by traditional histogram equalization exceeds the desired amount Which damages the image properties and wanes its natural look. Histogram division and performing a separate equalization for each sub-histogram is one of the presented solutions. The dividing method and determining the number of sub-histograms are the main problems directly affecting the output image quality. In this study, a method is introduced for automatic determination of the number of sub-histograms and density based histogram division leading to appropriate output with no need for parameter setting. Each main peak is in a separate section. Image contrast is increased with no loss of image specifications through determining the number of sub-histograms based on the number of main peaks. The introduced histogram equalization approach consists of three stages. The first stage, using histogram analysis, produces an automated estimate of number of clusters for image brightness levels. The second, clusters the image brightness levels, and using the provided transfer function, the final stage includes contrast enhancement for each individual cluster separately. The results of the proposed approach demonstrate not only clearer details along with a boost in contrast, but also noticeably more natural appearance in the images.     

Novel mean-shift based histogram equalization using textured regions

This paper presents a novel mean-shift based histogram equalization method called the MSHE method. The key insight of the proposed MSHE method is that the basis of histogram equalization could be based on textured regions in an image, while impact of smoother regions should be suppressed. Using a mean-shift based approach, the sets of textured regions in an image are determined by finding regions which have a high density of edge concentration. In addition, a new cost function is presented to balance the image quality and contrast enhancement effect for search termination in the proposed algorithm. Based on three typical test images, experimental results show that our proposed MSHE method is quite competitive with the previous eleven methods, such as the HE, BBHE, DSIHE, POHE, RSWHE, DHE, BPDHE, SRHE, GHE, FHE, and THShap.     

Segment dependent dynamic multi-histogram equalization for image contrast enhancement

Histogram equalization (HE) method proved to be a simple and most effective technique for contrast enhancement of digital images. However it does not preserve the brightness and natural appearance of the images, which is a major drawback. To overcome this limitation, several Bi- and Multi-HE methods have been proposed. Although the Bi-HE methods significantly enhance the contrast and may preserve the brightness, the natural appearance of the images is not preserved as these methods suffer with the problem of intensity saturation. While Multi-HE methods are proposed to further maintain the brightness and natural appearance of images, but at the cost of contrast enhancement. In this paper, two novel Multi-HE methods for contrast enhancement of natural images, while preserving the brightness and natural appearance of the images, have been proposed. The technique involves decomposing the histogram of an input image into multiple segments based on mean or median values as thresholds. The narrow range segments are identified and are allocated full dynamic range before applying HE to each segment independently. Finally the combined equalized histogram is normalized to avoid the saturation of intensities and un-even distribution of bins. Simulation results show that, for the variety of test images (120 images) the proposed method enhances contrast while preserving brightness and natural appearance and outperforms contemporary methods both qualitatively and quantitatively. The statistical consistency of results has also been verified through ANOVA statistical tool.     

Contrast enhancement and brightness preserving of digital mammograms using fuzzy clipped contrast-limited adaptive histogram equalization algorithm

A novel fuzzy logic and histogram based algorithm called Fuzzy Clipped Contrast-Limited Adaptive Histogram Equalization (FC-CLAHE) algorithm is proposed for enhancing the local contrast of digital mammograms. A digital mammographic image uses a narrow range of gray levels. The contrast of a mammographic image distinguishes its diagnostic features such as masses and micro calcifications from one another with respect to the surrounding breast tissues. Thus, contrast enhancement and brightness preserving of digital mammograms is very important for early detection and further diagnosis of breast cancer. The limitation of existing contrast enhancement and brightness preserving techniques for enhancing digital mammograms is that they limit the amplification of contrast by clipping the histogram at a predefined clip-limit. This clip-limit is crisp and invariant to mammogram data. This causes all the pixels inside the window region of the mammogram to be equally affected. Hence these algorithms are not very suitable for real time diagnosis of breast cancer. In this paper, we propose a fuzzy logic and histogram based clipping algorithm called Fuzzy Clipped Contrast-Limited Adaptive Histogram Equalization (FC-CLAHE) algorithm, which automates the selection of the clip-limit that is relevant to the mammogram and enhances the local contrast of digital mammograms. The fuzzy inference system designed to automate the selection of clip-limit requires a limited number of control parameters. The fuzzy rules are developed to make the clip limit flexible and variant to mammogram data without human intervention. Experiments are conducted using the 322 digital mammograms extracted from MIAS database. The performance of the proposed technique is compared with various histogram equalization methods based on image quality measurement tools such as Contrast Improvement Index (CII), Discrete Entropy (DE), Absolute Mean Brightness Coefficient (AMBC) and Peak Signal-to-Noise Ratio (PSNR). Experimental results show that the proposed FC-CLAHE algorithm produces better results than several state-of-art algorithms.     

一种广义直方图及其在彩色图像增强中的应用

提出了一种广义直方图的构造方法并将其用于彩色图像均衡化增强。针对传统直方图均衡化方法实现彩色图像增强并不具有普适性的不足,将传统灰度图像直方图定义进行修改并得到一种广义灰度图像直方图,将其用于彩色图像在HSV空间实现均衡化增强。实验结果表明,所建议的广义直方图均衡化彩色图像增强方法是有效的,且比传统直方图均衡化方法能取得更好的增强效果。     

薄雾条件下退化图像的直方图均衡模型

传统灰度图像直方图均衡模型在处理薄雾条件下的降质图像时,会出现概率较低的灰度级合并,总的灰度级有所损失问题,因此导致图像细节丢失,灰度分辨率下降,针对这一问题提出了改进的灰度图像直方图均衡模型,采取去除原始图像实际没有占有的灰度级,只对不为0的灰度级进行直方图均衡,从而保证总的灰度级数不减少,可以在一定程度上解决灰度级保持的问题。实验表明该直方图均衡模型可以对薄雾条件下降质图像处理取得较好的增强效果。     

Analog and digital image enhancement using fuzzy expert systems

 Image enhancement is a field that is being used in various areas and disciplines. Advances in computers, microcontrollers and DSP boards have opened new horizons to digital image processing, and have opened many avenues to the design and implementation of new innovative techniques. This paper compares image enhancement via the modification of the probability density function of the gray levels with the new techniques that involves the use of knowledge-base (fuzzy expert) systems that are capable of mimicking the behavior of a human expert. A fuzzy expert system based software for image enhancement, called SmartPhotoLab has been introduced for the above purpose. Present address: A. El-Osery Dept. of Electrical Engineering, New Mexico Tech, Workman Center Rm. 247 801 Leroy place, Socorro, NM 87801 e-mail: elosery@ee.nmt.edu. This work was supported in parts by NASA grants no. NAG2–1196 and 2-1480.     

An image segmentation method using histograms and the human characteristics of HSI color space for a scene image

Image segmentation is an important subject for image recognition. Here, we propose a new image segmentation method for scene images. The proposed segmentation method classifies images into several segments based on the human visual sense and achromatic color. We calculate the histograms of the image for each component of the hue, saturation, and intensity (HSI) color space, and obtain three results of image segmentation from each histogram. We consider achromatic colors in order to decrease the number of regions. We compare the results of the proposed method with those of the k-means methods for the effectiveness of the proposed method. This work was presented, in part, at the 10th International Symposium on Artificial Life and Robotics, Oita, Japan, February 4–6, 2005     

Relational image compression: optimizations through the design of fuzzy coders and YUV color space

For Image Compression and reconstruction method based on Fuzzy relational equations (ICF), two optimizations are proposed. First optimization is to propose an invariant index for the design of appropriate coders in ICF, we call an overlap level of fuzzy sets. Second optimization corresponds to application of YUV color space to the existing ICF. Through the experiment of image compression and reconstruction using 1000 test images extracted from Corel Gallery, the invariance of the overlap level is confirmed. Furthermore, by the experimental comparison of the proposed method (ICF over YUV color space) and the conventional one (ICF over RGB color space) using 1000 test images, it is confirmed that the Peak Signal to Noise Ratio of the proposed method is increased at a rate of 7.1%∼13.2% compared with the conventional one, under the condition that the compression rates are 0.0234∼0.0938.     

A real-time image processor with combining dynamic contrast ratio enhancement and inverse gamma correction for PDP

Inverse gamma correction must be performed before displaying the received video signal because alternating current plasma display panel (AC PDP) has a linear output luminance response to a digital-valued input. At the same time contrast ratio enhancement is necessary for improving the image quality of display devices. The histogram equalization (HE) is an important contrast ratio enhancement method. But sometimes HE can produce unrealistic effects in images. In this paper, a new method of combining dynamic contrast ratio enhancement and inverse gamma correction for AC PDP is proposed. The dynamic contrast ratio enhancement and the inverse gamma correction are realized simultaneously in the proposed method. Furthermore the over-enhancement caused by the traditional HE can be avoided. A real-time image processor with the proposed method was designed and implemented. Simulations and experimental results on a 50-in. AC PDP show that the image quality of AC PDP can be improved obviously.     

Contrast control for grey-level deformation removal in images: an algorithm and a computer application

An algorithm and a computer routines library written in object programming language C++ are described, which allow removal of global intensity deformation in a grey-scale image using contrast control. During the process, a Gaussian pyramid representation of an input image is constructed through low-pass filtration and sampling of successive pyramid levels, where the input image constitutes the first (zero) level of the pyramid. In the second step a Laplacian pyramid is built by subtracting successive levels of the Gaussian pyramid. Then, all levels in the Laplacian pyramid are expanded to the original image size and added with weights, which are real numbers, to reconstruct the image. Proper choice of the weights allows global grey-level deformation removal. This technique can be applied for contrast enhancement and image archiving of airborne, scanned, photo-copied and optical camera-made images, where global distortions of intensity are frequently met.     

Hand radiographs preprocessing, image representation in the finger regions and joint space width measurements for image interpretation

In this paper, the first stage of studies concerning the computer analysis of hand X-ray digital images is described. The images are preprocessed and then skeletization of the fingers is carried out. Then, the interphapangeal and metacarpophalangeal joints are detected and contoured. Joint widths are also measured. The obtained results largely concur with those obtained by other authors—see Beier et al. [Segmentation of medical images combining local, regional, global, and hierarchical distances into a bottom-up region merging scheme, Proc. SPIE 5747 (2005) 546-555], Klooster et al. [Automatic quantification of osteoarthritis in hand radiographs: validation of a new method to measure joint space width, Osteoarthritis and Cartilage 16 (1) (2008) 18-25], Ogiela et al. [Image languages in intelligent radiological palm diagnostics, Pattern Recognition 39 (2006) 2157-2165] and Ogiela and Tadeusiewicz [Picture languages in automatic radiological palm interpretation, Int. J. Appl. Math. Comput. Sci. 15 (2) (2005) 305-312].