Traditional Histogram Equalization Research Articles

Dual Histogram Equalization Algorithm Based on Adaptive Image Correction

For the visual measurement of moving arm holes in complex working conditions, a histogram equalization algorithm can be used to improve image contrast. To lessen the problems of image brightness shift, image over-enhancement, and gray-level merging that occur with the traditional histogram equalization algorithm, a dual histogram equalization algorithm based on adaptive image correction (AICHE) is proposed. To prevent luminance shifts from occurring during image equalization, the AICHE algorithm protects the average luminance of the input image by improving upon the Otsu algorithm, enabling it to split the histogram. Then, the AICHE algorithm uses the local grayscale correction algorithm to correct the grayscale to prevent the image over-enhancement and gray-level merging problems that arise with the traditional algorithm. It is experimentally verified that the AICHE algorithm can significantly improve the histogram segmentation effect and enhance the contrast and detail information while protecting the average brightness of the input image, and thus the image quality is significantly increased.

Nonlinear error reduction for phase-shifting profilometry considering periodicity and symmetry of a phase histogram.

Phase-shifting profilometry is extensively utilized for three-dimensional (3D) measurement. However, because of gamma nonlinearity, the image intensities of the captured fringe patterns are regrettably distorted. An effective nonlinear error reduction method without requiring parameter estimation is presented in this paper. Differing from the traditional whole-period phase histogram equalization (PHE) method, our method takes into account not only the periodicity but also the symmetry of the phase histogram. Taking a three-step phase-shifting algorithm as an example, the phase error frequency triples the fringe frequency; thus, we first propose a 1/3-period PHE method. Moreover, since the phase error distribution is sinusoidal with symmetry, we further propose a 1/6-period PHE method. Simulations and experiments both indicate that the 1/6-period PHE method, compared with the whole-period PHE and 1/3-period PHE methods, can further reduce the nonlinear error.

A low-light image enhancement method with brightness balance and detail preservation.

This paper proposes a new method for low-light image enhancement with balancing image brightness and preserving image details, this method can improve the brightness and contrast of low-light images while maintaining image details. Traditional histogram equalization methods often lead to excessive enhancement and loss of details, thereby resulting in an unclear and unnatural appearance. In this method, the image is processed bidirectionally. On the one hand, the image is processed by double histogram equalization with double automatic platform method based on improved cuckoo search (CS) algorithm, where the image histogram is segmented firstly, and the platform limit is selected according to the histogram statistics and improved CS technology. Then, the sub-histograms are clipped by two platforms and carried out the histogram equalization respectively. Finally, an image with balanced brightness and good contrast can be obtained. On the other hand, the main structure of the image is extracted based on the total variation model, and the image mask with all the texture details is made by removing the main structure of the image. Eventually, the final enhanced image is obtained by adding the mask with texture details to the image with balanced brightness and good contrast. Compared with the existing methods, the proposed algorithm significantly enhances the visual effect of the low-light images, based on human subjective evaluation and objective evaluation indices. Experimental results show that the proposed method in this paper is better than the existing methods.

A Post-Processing Algorithm for Boosting Contrast of MRI Images

Low contrast of Magnetic Resonance (MR) images limits the visibility of subtle structures and adversely affects the outcome of both subjective and automated diagnosis. State-of-the-art contrast boosting techniques intolerably alter inherent features of MR images. Drastic changes in brightness features, induced by post-processing are not appreciated in medical imaging as the grey level values have certain diagnostic meanings. To overcome these issues this paper proposes an algorithm that enhance the contrast of MR images while preserving the underlying features as well. This method termed as Power-law and Logarithmic Modification-based Histogram Equalization (PLMHE) partitions the histogram of the image into two sub histograms after a power-law transformation and a log compression. After a modification intended for improving the dispersion of the sub-histograms and subsequent normalization, cumulative histograms are computed. Enhanced grey level values are computed from the resultant cumulative histograms. The performance of the PLMHE algorithm is compared with traditional histogram equalization based algorithms and it has been observed from the results that PLMHE can boost the image contrast without causing dynamic range compression, a significant change in mean brightness, and contrast-overshoot.

Infrared image enhancement algorithm using local entropy mapping histogram adaptive segmentation

The traditional histogram equalization algorithm may cause problems such as local over-enhancement and noise amplification while enhancing the image. To solve these problems, this paper proposes an infrared image enhancement method using local entropy mapping histogram adaptive segmentation. Firstly, a local entropy mapping histogram is built through the modified ‘sigmoid’ function to describe the detailed distribution of the infrared image. Then the LOESS algorithm and local minimum examination are used to adaptively segment the local entropy mapping histogram into multiple sub-histograms. And follow, the double plateau constraint of shape preservation is adopted, and the double plateau thresholds of each interval of the local entropy mapping histogram are adaptively optimized according to the genetic algorithm. Finally, multiple sub-histograms are equalized to obtain an enhanced image. Comparative experiments on real infrared images show that our method is ahead of other superior methods in qualitative and quantitative evaluation.

A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image

At present, iris recognition has been widely used as a biometrics-based security enhancement technology. However, in some application scenarios where a long-distance camera is used, due to the limitations of equipment and environment, the collected iris images cannot achieve the ideal image quality for recognition. To solve this problem, we proposed a modified sparrow search algorithm (SSA) called chaotic pareto sparrow search algorithm (CPSSA) in this paper. First, fractional-order chaos is introduced to enhance the diversity of the population of sparrows. Second, we introduce the Pareto distribution to modify the positions of finders and scroungers in the SSA. These can not only ensure global convergence, but also effectively avoid the local optimum issue. Third, based on the traditional contrast limited adaptive histogram equalization (CLAHE) method, CPSSA is used to find the best clipping limit value to limit the contrast. The standard deviation, edge content, and entropy are introduced into the fitness function to evaluate the enhancement effect of the iris image. The clipping values vary with the pictures, which can produce a better enhancement effect. The simulation results based on the 12 benchmark functions show that the proposed CPSSA is superior to the traditional SSA, particle swarm optimization algorithm (PSO), and artificial bee colony algorithm (ABC). Finally, CPSSA is applied to enhance the long-distance iris images to demonstrate its robustness. Experiment results show that CPSSA is more efficient for practical engineering applications. It can significantly improve the image contrast, enrich the image details, and improve the accuracy of iris recognition.

Global–local image enhancement with contrast improvement based on weighted least squares

Contrast improvement as well as detail preservation is important for human visualization in image enhancement. Traditional histogram equalization methods are easy to produce over-enhancement results, resulting in the loss of fine details. In order to deal with this problem, this paper proposes an image enhancement scheme (called as global–local image enhancement, GLIE) based on weighted least squares (WLS) for contrast enhancement and structural preservation, which includes the following key parts: First, we employ the WLS method to separate a low-quality image into a base image and a detail image. Then, the base image is enhanced by adaptive gamma correction based on cumulative histogram. Next, the enhanced base image jointing with the original image is used to obtain an enhancement coefficient for correcting details. Finally, the high-quality image is generated by adding the enhanced base and detail images. Experimental results in both qualitation and quantitation verify that the proposed GLIE method is effective, and even is better than the state-of-the-arts.

ICEBIN: Image Contrast Enhancement Based on Induced Norm and Local Patch Approaches

Traditional histogram equalization may cause degraded results of over-enhanced images under uneven illuminations. In this paper, a simple and effective image contrast enhancement method is proposed to achieve high dynamic range imaging. First, the illumination of each pixel is estimated by using an induced norm of a patch of the image. Second, a pre-gamma correction is proposed to enhance the contrast of the illumination component appropriately. The parameters of gamma correction are set dynamically based on the local patch of the image. Third, an automatic Contrast-Limited Adaptive Histogram Equalization (CLAHE) whose clip point is automatically set is applied to the processed image for further image contrast enhancement. Fourth, a noise reduction algorithm based on the local patch is developed to reduce image noise and increase image quality. Finally, a post-gamma correction is applied to slightly enhance the dark regions of images and not affect the brighter areas. Experimental results show that the proposed method has its superiority over several state-of-the-art enhancement quality techniques by using qualitative and quantitative evaluations.

COMPARISON OF IMAGE ENHANCEMENT TECHNIQUES FOR RAPID PROCESSING OF POST FLOOD IMAGES

Abstract. Satellite images are widely used for assessing the areal extent of flooded areas. However, presence of clouds and shadow limit the utility of these images. Numerous digital algorithms are available for enhancing such images and highlighting areas of interest. These algorithms range from simple to complex, and the time required to process these images also varies considerably. For disaster response, it is important to select an algorithm that can enhance the quality of the images in relatively short time. This study compared the relative performance of five traditional (Histogram Equalization, Local Histogram Equalization, Contrast Limited Adaptive Histogram Equalization, Gamma Correction, and Linear Contrast Stretch) algorithms for enhancing post-flood satellite images. Flood images with different levels of clouds and shadows were enhanced and output generated were evaluated in terms of processing time and quality as measured by Blind/Reference less Image Spatial Quality Evaluator (BRISQUE), a no-reference image quality metric. Findings from this study will provide valuable information to image analysts for selecting a suitable algorithm for rapidly processing post-flood satellite images.

Image enhancement with the preservation of brightness and structures by employing contrast limited dynamic quadri-histogram equalization

Image enhancement has been widely applied to medical science, industry, agriculture, and military, for which it is very important to preserve brightness and structures. In order to overcome the shortcomings of over-enhancement and over-smoothing of traditional histogram equalization methods, this paper proposes an effective image enhancement strategy named as contrast limited dynamic quadri-histogram equalization (CLDQHE) which includes three steps: (1) the original histogram of an image is divided by a proposed threshold scheme into four sub-histograms. (2) An adaptive histogram clipping method is proposed to control the enhancement rate, which can adjust the clipping threshold. (3) Each sub-histogram is mapped to a new dynamic range and is equalized independently. Both quantitatively and qualitatively experimental results demonstrate that the proposed CLDQHE method can yield pleasing results with the preservation of brightness and structures, and even outperforms the state-of-the-arts.

A Mobile Robot Recognize Blurred Targets via an Improved Histogram Equalization

Target recognition is a precondition for guiding robots to complete tasks. However, if the target is blurred, recognition will be difficult, which will lead to an inefficiency operation for robots. For the sake of improving the robot operation efficiency, it is essential to improve the image quality of recognized blurred target. Since image enhancement is taken as a powerful ways for improving image quality, it has gained largely attentions from researches. In fact, most of enhance methods are proposed based on histogram equalization, and until present, focus of research is still on improvements of histogram equalization. Hence, this paper proposes an improved histogram equalization for blurred targets in mobile robot recognition. Compared with the traditional histogram equalization methods, our method has less calculation time and better performance of real-time. Furthermore, the proposed approach also has strong robustness in blurred target recognition via a mobile robot.

红外图像自适应逆直方图增强技术

In infrared images, when the traditional histogram equalizes the image, the detail pixels are easily immerged by the background pixels, resulting in the image being too bright and too dark. Based on this situation, an adaptive inverse histogram equalization algorithm was proposed in this paper. The algorithm enhanced image details by inverse statistics, adaptive selection threshold and segmentation mapping. Compared with the traditional histogram equalization algorithm, the inverse histogram equalization algorithm significantly improve the image visual effect in different gray level distributions and enhance the details of different areas of the image to different degrees. Moreover, under the premise of achieving better image processing effects, this algorithm can still guarantee real-time performance and high efficiency by optimizing calculation methods, and is suitable for FPGA hardware transplantation.

Cuckoo search algorithm-based brightness preserving histogram scheme for low-contrast image enhancement

This paper introduces a novel optimized brightness preserving histogram equalization approach to preserve the mean brightness and to improve the contrast of low-contrast image using cuckoo search algorithm. Traditional histogram equalization scheme induces extreme enhancement and brightness change ensuing abnormal appearance. The proposed method utilizes plateau limits to modify histogram of the image. In this method, histogram is divided into two sub-histograms on which histogram statistics are exploited to obtain the plateau limits. The sub-histograms are equalized and modified based on the calculated plateau limits obtained by cuckoo search optimization technique. To demonstrate the effectiveness of proposed method a comparison of the proposed method with different histogram processing techniques is presented. Proposed method outperforms other state-of-art methods in terms of the objective as well as subjective quality evaluation.

A logarithmic law based histogram modification scheme for naturalness image contrast enhancement

In this paper, a new logarithmic histogram modification technique for image contrast enhancement with naturalness preservation has been proposed. Traditional histogram equalization scheme usually causes extreme contrast enhancement, which results in unnatural look and artifacts. The proposed technique first enhances the contrast of the image globally through addition and logarithmic law based modification scheme, thereafter the local details of the image are emphasized through the coefficient scaling directly in the compressed domain using discrete cosine transformation. The proposed method can enhance the image contrast uniformly with less number of parameters without losing its basic features. Experimental results show that the proposed method preserves the natural appearance of the image and yields better perceptual quality as compared to the state-of-the-art techniques.

Feature Extraction of Remote Sensing Images Based on Bat Algorithm and Normalized Chromatic Aberration

These accurate extraction of specific objects in remote sensing images has become a research hotspot. For remote sensing image feature extraction, shape, color and other features can be selected to extract objects from complex scenes. In this paper, a method of remote sensing image feature extraction based on bat algorithm and normalized chromatic aberration is proposed. Firstly, the contrast of remote sensing images is enhanced by using bat algorithm. After enhancement, it can be seen from the histogram that the optimized images contrast is significantly enhanced compared with the traditional histogram equalization. Then, the normalized chromatic aberration method is adopted to extract features. The normalized chromatic aberration is calculated by normalizing the RGB three-channel component and compared with the fixed threshold. Finally, the feature binary graphs are obtained, and then the region of interest (ROI) in the remote sensing image is extracted. The algorithm proposed in this paper can realize remote telematics sensing images processing and obtain complete and accurate target areas. The highest extraction rate was reached 96%.

Density-Based Histogram Partitioning and Local Equalization for Contrast Enhancement of Images

Histogram Equalization technique is one of the basic methods in image contrast enhancement. Using this method, in the case of images with uniform gray levels (with narrow histogram), causes loss of image detail and the natural look of the image. To overcome this problem and to have a better image contrast enhancement, a new two-step method was proposed. In the first step, the image histogram is partitioned into some sub-histograms according to mean value and standard deviation, which will be controlled with PSNR measure. In the second step, each sub-histogram will be improved separately and locally with traditional histogram equalization. Finally, all sub-histograms will be combined to obtain the enhanced image. Experimental results shows that this method would not only keep the visual details of the histogram, but also enhance image contrast.

An effective histogram modification scheme for image contrast enhancement

Contrast enhancement plays a significant role in digital image processing. However, traditional histogram equalization usually results in excessive enhancement, which in return causes an unnatural look and loss of details to the target image. In this paper, we propose a novel histogram modification scheme for image contrast enhancement. First, sum of the input histogram and its standard deviation are computed. Then, gamma correction is applied on the result sum to generate a modified histogram. And finally, the traditional histogram equalization is applied on the modified histogram to produce the mapping function. In addition to preserving the mean brightness, the proposed method can enhance an image uniformly with low computation complexity. Extensive experimental results show that the proposed method not only retains features of the input image but also can enhance the contrast of all kind of images significantly.

Combination of histogram segmentation and modification to preserve the original brightness of the images

Image enhancement by preserving the original brightness is the main challenge of the consumer electronics field. This paper concentrates on the modification of traditional histogram equalization (HE) method to increase its brightness preserving ability. In literature mainly two types of variants have been proposed based on segmentation and modification of the histogram. Both variants are able to preserve the original brightness to some extent. Actually the efficiency of these variants of HE depend on the optimal segmentation and proper modification of histogram. This study concentrates to prove that histogram segmentation or modification which one is better to preserve the image's original brightness and how much it can be preserved by using the combination of two variants. New hybrid variants of HE method have been proposed in this paper to prove that fact. Results of the proposed methods have been analyzed visually and mathematically.

基于Retinex理论的草地图像增强算法研究

为了解决室外环境中由于光照不均或者大雾天气下，草地图像模糊不清，不能有效的识别和提取草地图像重要信息的问题，本文提出一种专门针对草地图像的图像增强算法。本文基于传统的Retinex理论，用小波变换的方法拆分图像的高频分量和低频分量分别进行处理，之后再将处理结果融合重构。实验结果表明，相比传统的MSRCR算法和直方图均衡化方法，本文的算法提高了图像清晰度并且有效的抑制了图像处理时产生的噪声。 The article proposes a kind of image enhancement algorithm for lawn image, in order to solve the problem that lawn image is so blurred due to uneven illumination or heavy fog weather that it can’t identify and extract the important information effectively in outdoor environments. This paper uses wavelet transform method based on the traditional Retinex theory. It split the high frequency component and low frequency component then deal with them separately. Finally, the results are fused and reconstructed. Experimental results show that the algorithm of this paper provides the image’s definition and controls noise in image processing when it compared with traditional MSRCR algorithm and histogram equalization algorithm.

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.