Contrast Enhancement Algorithm Research Articles

Expeditious Contrast Enhancement for Grayscale Images Using a New Swift Algorithm

Contrast enhancement plays a significant role in many existing image-related applications. In various situations, conventional contrast enhancement techniques failed to produce acceptable results for a wide variety of low-contrast images. As a result, various innovative techniques have been proposed for the purpose of contrast enhancement. Despite that, this field is still open for research due to its indispensability in many scientific disciplines and to various unavoidable real-world limitations. Hence, this article introduces a novel swift algorithm for contrast enhancement in images of low-contrast. The processing concept of this algorithm is straightforward. Initially, a non-complex logarithmic function is applied as a preprocessing step to attenuate the immoderate pixel values. Then, a new non-linear enhancement function which is designed experimentally based on mathematical, statistical and spatial information is applied to modify the brightness and contrast. Finally, a regularization function is applied as a post-processing step to rearrange the image pixels into their natural dynamic range. Experimental results revealed the favorability of the proposed algorithm, as it provided better results than those produced by several contemporary techniques in terms of recorded accuracy and perceived quality.

Contrast enhancement using triple dynamic clipped histogram equalization based on mean or median

Many methods based on the histogram equalization have been introduced for the use in contrast enhancement. However, a technique that can produce proper natural enhancement simultaneously in images with brightness ranges of dark, medium, and bright, has been less presented. For this purpose, a powerful contrast enhancement algorithm based on the histogram equalization, called triple dynamic clipped histogram equalization (TDCHE) method has been proposed in this paper. In the proposed method, the histogram of the input image is first partitioned based on the mean or median into three portions. Then, the histogram clipping process is performed in each sub-histogram. Finally, prior to performing the equalization process of each sub-histogram independently, each sub-histogram is mapped to a new dynamic range. The proposed method is introduced to achieve multiple objectives of maximum average information content (entropy), enhancement rate control and reasonable brightness preservation. In addition, this method leads to natural enhancement by producing clear images and preserving maximum details. Performance assessment of the proposed method in terms of the entropy, structural similarity index as well as visual quality based on mean opinion score (MOS) demonstrates perceived superiority of the proposed algorithm in comparison with the previously presented techniques.

Adaptive Image Enhancement Using Entropy-Based Subhistogram Equalization.

A novel image enhancement approach called entropy-based adaptive subhistogram equalization (EASHE) is put forward in this paper. The proposed algorithm divides the histogram of input image into four segments based on the entropy value of the histogram, and the dynamic range of each subhistogram is adjusted. A novel algorithm to adjust the probability density function of the gray level is proposed, which can adaptively control the degree of image enhancement. Furthermore, the final contrast-enhanced image is obtained by equalizing each subhistogram independently. The proposed algorithm is compared with some state-of-the-art HE-based algorithms. The quantitative results for a public image database named CVG-UGR-Database are statistically analyzed. The quantitative and visual assessments show that the proposed algorithm outperforms most of the existing contrast-enhancement algorithms. The proposed method can make the contrast of image more effectively enhanced as well as the mean brightness and details well preserved.

Single image veiling glare removal

ABSTRACTPhotographing against a bright light source is difficult since the direct light from the light source causes glare phenomenon in the lens, changing the colour balance and reducing the image contrast within the field of view. To address the effect, we introduce a glare formation model that accounts for the characters of the glare. Based on the model, we propose a single image processing method to remove the glare in the images by decomposing the image into the scene layer and the glare layer. We then adjust the brightness and the colour balance of the scene layer to eliminate the influences of the glare. In order to enhance the contrast decreased by the glare, we use a local standard deviation-based contrast enhancement algorithm to boost the details while avoiding excessive enhancement. Experimental results show that the method removes the majority of the glare and improves the local contrast effectively.

An approach for de-noising and contrast enhancement of retinal fundus image using CLAHE

Now-a-days medical fundus images are widely used in clinical diagnosis for the detection of retinal disorders. Fundus images are generally degraded by noise and suffer from low contrast issues. These issues make it difficult for ophthalmologist to detect and interpret diseases in fundus images. This paper presents a noise removal and contrast enhancement algorithm for fundus image. Integration of filters and contrast limited adaptive histogram equalization (CLAHE) technique is applied for solving the issues of de-noising and enhancement of color fundus image. The efficacy of the proposed method is evaluated through different performance parameters like Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), Correlation coefficient (CoC) and Edge preservation index (EPI). The proposed method achieved 7.85% improvement in PSNR, 1.19% improvement in SSIM, 0.12% improvement in CoC and 1.28% improvement in EPI when compared to the state of the art method.

An improved contrast enhancement algorithm for infrared images based on adaptive double plateaus histogram equalization

Infrared thermal images can reflect the thermal-radiation distribution of a particular scene. However, the contrast of the infrared images is usually low. Hence, it is generally necessary to enhance the contrast of infrared images in advance to facilitate subsequent recognition and analysis. Based on the adaptive double plateaus histogram equalization, this paper presents an improved contrast enhancement algorithm for infrared thermal images. In the proposed algorithm, the normalized coefficient of variation of the histogram, which characterizes the level of contrast enhancement, is introduced as feedback information to adjust the upper and lower plateau thresholds. The experiments on actual infrared images show that compared to the three typical contrast-enhancement algorithms, the proposed algorithm has better scene adaptability and yields better contrast-enhancement results for infrared images with more dark areas or a higher dynamic range. Hence, it has high application value in contrast enhancement, dynamic range compression, and digital detail enhancement for infrared thermal images.

Visibility enhancement of images degraded by hazy weather conditions using modified non-local approach

Natural images captured under bad weather situations suffer from poor visibility and contrast problems. Object tracking and recognition under hazy bad weather conditions is a very difficult task for real time applications. Therefore, in this paper, we have proposed an efficient dehazy algorithm for visibility and contrast enhancement of color hazy images. The proposed algorithm works in two phases. In the first phase, a non local approach is applied in the hazy model, which is a pixel based approach not a patch based. Pixels are spread over the entire image plane and positioned at different distance from the sensor, so this approach is called non local. Degradation is different for every pixel therefore; estimation of the transmission map for every pixel through the haze line is the essential step. After the first phase, the image becomes unnatural and dimmed, therefore to proper tone mapping and improving the visual quality of the image, we applied the S-shaped mapping function in the second phase. The quantitative results of the proposed algorithm and other existing dehazy algorithms for color hazy images are obtained in terms of Hazy Reduction factor(HRF), and measure of enhancement factor(EMF) on different hazy image databases. Qualitative results reveal that the visual quality of the proposed algorithm is better than other existing de-hazy algorithms. Simulation results demonstrate that the proposed algorithm provides better results as compared to other existing dehazy algorithms for color hazy images. Proposed algorithm is highly efficient as compare to other latest dehazy algorithms.

Contrast enhancement for cDNA microarray image based on fourth-order moment

Microarray technology, which can monitor the expression levels of thousands of genes simultaneously, has been widely used in biological experiment. Image processing, as one key step in microarray technology, plays an essential role in microarray analysis. Meanwhile, biological applications require a higher accuracy in each image processing step. However, the low contrast levels of image make it difficult to obtain better processing precision. This paper proposes a fully automatic contrast enhancement (CE) method based on fourth-order moment. Also, a background estimation method is presented to obtain a better microarray image processing result. Comparative results on contrast enhance and gridding revealed that the proposed CE algorithm performs better compared to the adaptive histogram equalization method. Numerous experiments on the Swiss Institute of Bioinformatics (SIB), Joe DeRisi individual (DeRisi), Gene Expression Omnibus (GEO), and Stanford Microarray Database (SMD) data sets also indicate that the proposed CE exerts a tremendous effect on gridding, but has nothing to do with segmentation.

Local entropy maximization based image fusion for contrast enhancement of mammogram

Early detection of breast cancer can enhance the chances of survival. Contrast enhancement provides improved visual information that can be used for accurate segmentation of mammogram images. This research work presents DWT coefficient fusion based on local entropy maximization algorithm for contrast enhancement. In this algorithm, the original image and CLAHE contrast-enhanced image are decomposed for three levels using Haar wavelet. At each level of decomposition, approximate coefficients are fused using averaging operation and detailed coefficients are fused by calculating entropy of 5x5 sliding window and choosing pixel value corresponding to maximum entropy. Finally, the mammogram image is reconstructed by combining approximate and detailed coefficients. The proposed algorithm is evaluated with performance metrics such as edge contents (EC), edge-based contrast measure (EBCM), featuresimilarity index measure (FSIM) and absolute mean brightness error (AMBE) with publically available MIAS (Mammographic Image Analysis Society) dataset comprising 322 images and TMCH (Tata Memorial Cancer Hospital)dataset consisting 100 images. The proposed algorithm shows improvement in EC value from 1.24 to 1.87, EBCM value from 64.24 to 120.1, FSIM 0.97 which is nearer to 1 and AMBE value of 2.01 which is very less. The proposed method is compared with most popular contrast enhancement methods HE, BBHE, CLAHE. The results showed that the proposed method offerssuperior results in terms of contrast and brightness.

Partial differential equation-based hazy image contrast enhancement

This study describes a modified partial differential equation (PDE)-based algorithm for contrast enhancement of hazy images. The formulation is supported by similarities between haze and low light conditions. The proposed approach employs multi-scale, local-global enhancement of inverted log reflectance and illumination components. These multiple processes are combined in a suitable PDE framework for improved effectiveness. Furthermore, the de-hazing process is automated via gradient-based optimization, eliminating the manual determination of PDE stopping time. Solutions are also devised for visual halo effect, over-enhancement and colour distortion. Based on subjective and quantitative analysis, the proposed technique consistently outperforms several de-hazing algorithms from the literature.

A Low-Complexity Algorithm for Contrast Enhancement of Digital Images

A Low-Complexity Algorithm for Contrast Enhancement of Digital Images

Acceleration of histogram‐based contrast enhancement via selective downsampling

The authors propose a general framework to accelerate the universal histogram-based image contrast enhancement (CE) algorithms. Both spatial and grey-level selective downsampling of digital images are adopted to decrease computational cost, while the visual quality of enhanced images is still preserved and without apparent degradation. Mapping function calibration is proposed to reconstruct the pixel mapping on the grey levels missed by downsampling. As two case studies, the accelerations of histogram equalisation (HE) and the state-of-the-art global CE algorithm, i.e. spatial mutual information and PageRank (SMIRANK), are presented in detail. Both quantitative and qualitative assessment results have verified the effectiveness of their proposed CE acceleration framework. In typical tests, the computational efficiencies of HE and SMIRANK have been increased by about 3.9 and 13.5 times, respectively.

Standard Intensity Deviation Approach based Clipped Sub Image Histogram Equalization Algorithm for Image Enhancement

The limitations of the hardware and dynamic range of digital camera have created the demand for post processing software tool to improve image quality. Image enhancement is a technique that helps to improve finer details of the image. This paper presents a new algorithm for contrast enhancement, where the enhancement rate is controlled by clipped histogram approach, which uses standard intensity deviation. Here standard intensity deviation is used to divide and equalize the image histogram. The equalization processes is applied to sub images independently and combine them into one complete enhanced image. The conventional histogram equalization stretches the dynamic range which leads to a large gap between adjacent pixels that produces over enhancement problem. This drawback is overcome by defining standard intensity deviation value to split and equalize the histogram. The selection of suitable threshold value for clipping and splitting image, provides better enhancement over other methods. The simulation results show that proposed method out performs other conventional histogram equalization (HE) methods and effectively preserves entropy.

Contrast Enhancement Algorithm for Outdoor Infrared Images Based on Local Gradient-Grayscale Statistical Feature

Contrast enhancement for infrared images is important in various night vision applications. However, existing local contrast enhancement algorithms often over-enhance smooth regions in outdoor infrared images. To address this limitation, this paper presents a contrast enhancement algorithm based on local gradient-grayscale statistical feature. The proposed algorithm first extracts such features from image sub-blocks, then classifies the sub-blocks as either simple or non-simple based on textural complexity using a model trained by a support vector machine, and subsequently adopts different grayscale mapping strategies to process the two types separately. Experimental results show that the proposed algorithm avoids over-enhancing simple regions while effectively improving the contrast in regions with more details.

Adaptive video contrast enhancement with low noise amplification via local data analysis

Unlike global enhancement methods, the proposed algorithm analyses an image in local areas to take full advantage of the local information, and enhances it in two channels to obtain exact result. Furthermore, the algorithm can restrain noise amplification by virtue of local statistic characteristics analysis. To enhance videos rapidly, the Kullback-Leibler distance between frames is used to characterised its similarity, based on it, enhancement function can be updated selectively. Experimental results show that the resultant images from the proposed algorithm are comparable or better than those from previous state-of-the-art methods. On the other hand, the computational complexity of the proposed method is much lower than the current local-data-based contrast enhancement algorithms.

Adaptive video contrast enhancement with low noise amplification via local data analysis

Unlike global enhancement methods, the proposed algorithm analyses an image in local areas to take full advantage of the local information, and enhances it in two channels to obtain exact result. Furthermore, the algorithm can restrain noise amplification by virtue of local statistic characteristics analysis. To enhance videos rapidly, the Kullback-Leibler distance between frames is used to characterised its similarity, based on it, enhancement function can be updated selectively. Experimental results show that the resultant images from the proposed algorithm are comparable or better than those from previous state-of-the-art methods. On the other hand, the computational complexity of the proposed method is much lower than the current local-data-based contrast enhancement algorithms.

A Color Restoration Method for Irreversible Thermal Paint Based on Atmospheric Scattering Model

Irreversible thermal paints or temperature sensitive paints are a kind of special temperature sensor which can indicate the temperature grad by judging the color change and is widely used for off-line temperature measurement during aero engine test. Unfortunately, the hot gases flow within the engine during measuring always make the paint color degraded, which means a serious saturation reduction and contrast loss of the paint colors. This phenomenon makes it more difficult to interpret the thermal paint test results. Present contrast enhancement algorithms can significantly increase the image contrast but can't protect the hue feature of the paint images effectively, which always cause color shift. In this paper, we propose a color restoration method for thermal paint image. This method utilizes the atmospheric scattering model to restore the lost contrast and saturation information, so that the hue can be protected and the temperature can be precisely interpreted based on the image.

Underwater image enhancement through depth estimation based on random forest

Light absorption and scattering in underwater environments can result in low-contrast images with a distinct color cast. This paper proposes a systematic framework for the enhancement of underwater images. Light transmission is estimated using the random forest algorithm. RGB values, luminance, color difference, blurriness, and the dark channel are treated as features in training and estimation. Transmission is calculated using an ensemble machine learning algorithm to deal with a variety of conditions encountered in underwater environments. A color compensation and contrast enhancement algorithm based on depth information was also developed with the aim of improving the visual quality of underwater images. Experimental results demonstrate that the proposed scheme outperforms existing methods with regard to subjective visual quality as well as objective measurements.

Contrast enhancement using feature-preserving bi-histogram equalization

A new contrast enhancement algorithm is proposed, which is based on the fact that, for conventional histogram equalization, a uniform input histogram produces an equalized output histogram. Hence before applying histogram equalization, we modify the input histogram in such a way that it is close to a uniform histogram as well as the original one. Thus, the proposed method can improve the contrast while preserving original image features. The main steps of the new algorithm are adaptive gamma transform, exposure-based histogram splitting, and histogram addition. The object of gamma transform is to restrain histogram spikes to avoid over-enhancement and noise artifacts effect. Histogram splitting is for preserving mean brightness, and histogram addition is used to control histogram pits. Extensive experiments are conducted on 300 test images. The results are evaluated subjectively as well as by DE, PSNR EBCM, GMSD, and MCSD metrics, on which, except for the PSNR, the proposed algorithm has some improvements of 2.89, 9.83, 28.32, and 26.38% over the second best ESIHE algorithm, respectively. That is to say, the overall image quality is better.

A Fractional-Order Variational Framework for Retinex: Fractional-Order Partial Differential Equation-Based Formulation for Multi-Scale Nonlocal Contrast Enhancement with Texture Preserving.

This paper discusses a novel conceptual formulation of the fractional-order variational framework for retinex, which is a fractional-order partial differential equation (FPDE) formulation of retinex for the multi-scale nonlocal contrast enhancement with texture preserving. The well-known shortcomings of traditional integer-order computation-based contrast-enhancement algorithms, such as ringing artefacts and staircase effects, are still in great need of special research attention. Fractional calculus has potentially received prominence in applications in the domain of signal processing and image processing mainly because of its strengths like long-term memory, nonlocality, and weak singularity, and because of the ability of a fractional differential to enhance the complex textural details of an image in a nonlinear manner. Therefore, in an attempt to address the aforementioned problems associated with traditional integer-order computation-based contrast-enhancement algorithms, we have studied here, as an interesting theoretical problem, whether it will be possible to hybridize the capabilities of preserving the edges and the textural details of fractional calculus with texture image multi-scale nonlocal contrast enhancement. Motivated by this need, in this paper, we introduce a novel conceptual formulation of the fractional-order variational framework for retinex. First, we implement the FPDE by means of the fractional-order steepest descent method. Second, we discuss the implementation of the restrictive fractional-order optimization algorithm and the fractional-order Courant-Friedrichs-Lewy condition. Third, we perform experiments to analyze the capability of the FPDE to preserve edges and textural details, while enhancing the contrast. The capability of the FPDE to preserve edges and textural details is a fundamental important advantage, which makes our proposed algorithm superior to the traditional integer-order computation-based contrast enhancement algorithms, especially for images rich in textural details.