Color Image Contrast Enhancement Research Articles

Variational Model with Nonstandard Growth Condition in Image Restoration and Contrast Enhancement

AbstractWe propose a new variational model in Sobolev-Orlicz spaces with non-standard growth conditions of the objective functional and discuss its applications to the simultaneous contrast enhancement and denoising of color images. The characteristic feature of the proposed model is that we deal with a constrained non-convex minimization problem that lives in variable Sobolev-Orlicz spaces where the variable exponent is unknown a priori and it depends on a particular function that belongs to the domain of the objective functional. In contrast to the standard approach, we do not apply any spatial regularization to the image gradient. We discuss the consistency of the variational model, give the scheme for its regularization, derive the corresponding optimality system, and propose an iterative algorithm for practical implementations.

Reversible data hiding based on global and local automatic contrast enhancement of low-light color images

Currently, many scholars have investigated reversible data hiding (RDH) based on automatic contrast enhancement (ACE). For RDH based on ACE (ACERDH), various methods have been proposed on how to better improve image quality. Preserving brightness can prevent the image from being over-enhanced. However, some ACERDH methods that can preserve brightness well cannot sufficiently enhance low-light images. Although some ACERDH methods that cannot preserve brightness well can more sufficiently enhance low-light color images, they lack the consideration of the local region, which is not conducive to enhancing the contrast of the local region. Therefore, a novel RDH method based on global ACE and local ACE is proposed. The global contrast is first improved by equalizing the global histogram. Then the high complexity region is optimized by the proposed RDH method based on double-layer ACE to further enhance the local contrast. The low-light color image can be well enhanced by applying the proposed method to enhance the R, G, and B channels sequentially. Experimental results demonstrated that the proposed method is superior to some existing advanced methods in obtaining better image quality and hiding more secret data.

A novel slime mold algorithm for grayscale and color image contrast enhancement

Image enhancement is a key step in image pre-processing. To address the problem of low quality and visual effect of images under low illumination conditions, this paper proposes an image enhancement method with hyperbolic oscillation factor and quadratic interpolation of slime mold algorithm (SSMA) in non-complete beta function dynamically looking to adjust the grayscale curve. The new strategy mainly proposes three different improvement strategies for the problem that the classical slime mold algorithm has low convergence accuracy and can easily fall into local optimum. Experimenting the proposed SSMA with other conventional and latest algorithms on the CEC2017 benchmark function and low-illumination standard dataset. The experimental results show that the convergence accuracy and convergence speed of SSMA are better than other algorithms. The proposed SSMA-optimized image enhancement algorithm effectively enhances the image brightness while preserving more details of the image, which is significantly better than other algorithms for image enhancement.

A histogram equalization model for color image contrast enhancement

A histogram equalization model for color image contrast enhancement

A color image contrast enhancement method based on improved PSO.

Image contrast enhancement uses the object intensity transformation function to maximize the amount of information to enhance an image. In this paper, the image enhancement problem is regarded as an optimization problem, and the particle swarm algorithm is used to obtain the optimal solution. First, an improved particle swarm optimization algorithm is proposed. In this algorithm, individual optimization, local optimization, and global optimization are used to adjust the particle's flight direction. In local optimization, the topology is used to induce comparison and communication between particles. The sparse penalty term in speed update formula is added to adjust the sparsity of the algorithm and the size of the solution space. Second, the three channels of the color images R, G, and B are represented by a quaternion matrix, and an improved particle swarm algorithm is used to optimize the transformation parameters. Finally, contrast and brightness elements are added to the fitness function. The fitness function is used to guide the particle swarm optimization algorithm to optimize the parameters in the transformation function. This paper verifies via two experiments. First, improved particle swarm algorithm is simulated and tested. By comparing the average values of the four algorithms under the three types of 6 test functions, the average value is increased by at least 15 times in the single-peak 2 test functions: in the multi-peak and multi-peak fixed-dimension 4 test functions, this paper can always search for the global optimal solution, and the average value is either the same or at least 1.3 times higher. Second, the proposed algorithm is compared with other evolutionary algorithms to optimize contrast enhancement, select images in two different data sets, and calculate various evaluation indicators of different algorithms under different images. The optimal value is the algorithm in this paper, and the performance indicators are at least a 5% increase and a minimum 15% increase in algorithm running time. Final results show that the effects the proposed algorithm have obvious advantages in both subjective and qualitative aspects.

Artificial Bee Colony Optimized MultiHistogram Equalization for Contrast Enhancement and Brightness Preservation of Color Images

Artificial Bee Colony Optimized MultiHistogram Equalization for Contrast Enhancement and Brightness Preservation of Color Images

Hybrid enhanced whale optimization algorithm for contrast and detail enhancement of color images

Hybrid enhanced whale optimization algorithm for contrast and detail enhancement of color images

Digital Color Image Contrast Enhancement Method Based on Luminance Weight Adjustment

Digital Color Image Contrast Enhancement Method Based on Luminance Weight Adjustment

Contrast-Aware Color Consistency Correction for Multiple Images

Color consistency optimization for multiple images is a challenging task in remote sensing and computer vision. To ensure that the visual quality of corrected images is satisfactory, not only should the color discrepancies between multiple images be invisible, but also the contrast of individual images should be visually appealing. Most color correction approaches focus on eliminating drastic color discrepancies, but ignore the problem of image contrast enhancement. Some color correction approaches even tend to degrade the image contrast to ensure that the image tones are consistent especially when the contrast of input images is low. To solve this problem, we present a contrast-aware color consistency correction approach in this paper. We attempt to eliminate the drastic color differences and enhance the contrast of input images simultaneously. We creatively integrate the problems of color consistency correction and image contrast enhancement into the same global energy optimization framework, and we also design a special cost function to minimize the color discrepancies and enhance the image contrast using the original color information. Thus, although the contrast of input images is low, our approach can still generate the corrected images with consistent tones and visually appealing contrast. At last, we select several challenging datasets to evaluate our approach. The experimental results visually and quantitatively demonstrate the effectiveness and superiority of the proposed contrast-aware color consistency correction approach. The results also demonstrate that our approach significantly outperforms the existing approaches, especially when the contrast of the input images is low.

Rapid Contrast Enhancement Algorithm for Natural Contrast- Distorted Color Images

Digital images are often obtained with contrast distortions due to different factors that cannot be avoided on many occasions. Various research works have been introduced on this topic, yet no conclusive findings have been made. Therefore, a low-intricacy multi-step algorithm is developed in this study for rapid contrast enhancement of color images. The developed algorithm consists of four steps, in that the first two steps include separate processing of the input image by the probability density function of the standard normal distribution and the softplus function. In the third step, the output of these two approaches is combined using a modified logarithmic image processing approach. In the fourth step, a gamma-controlled normalization function is applied to fully stretch the image intensities to the standard interval and correct its gamma. The results obtained by the developed algorithm have an improved contrast with preserved brightness and natural colors. The developed algorithm is evaluated with a dataset of various natural contrast degraded color images, compared against six different techniques, and assessed using three specialized image evaluation methods, in that the proposed algorithm performed the best among the comparators according to the used image evaluation methods, processing speed and perceived quality.

Image Contrast Optimization using Local Color Correction and Fuzzy Intensification

Global image enhancement techniques are used to enhance contrast in images but these techniques are found to be under-enhanced or over-enhanced in differently illuminated regions of the image. Local color correction methods work on local pixel regions to optimize the color contrast enhancement but they also have been found to show a lag while covering pixel regions which are overexposed, compared to those which are underexposed causing local artifacts. In this work, we overcome the shortcomings of both the local color correction and global color correction. This method uses local color correction in the Hue Saturation Luminance (HSL) domain, and fuzzy intensification operators are used to control the color fidelity of the local color corrected images. Thus, is able to sort out the problem of overexposed and underexposed regions and provide optimized contrast enhancement in colored images. Several experiments have been performed to analyze the performance of the proposed method and feasibility as compared to existing techniques. Performance parameters such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM) and Naturalness Image Quality Evaluator (NIQE) is evaluated and the comparison with some existing techniques of contrast enhancement of color images is performed. The obtained result have good contrast and approve the better performance of the proposed method in support of the quantitative measure of perceptual appearance of the processed images and low computational time.

A variational gamma correction model for image contrast enhancement

Image contrast enhancement plays an important role in computer vision and pattern recognition by improving image quality. The main aim of this paper is to propose and develop a variational model for contrast enhancement of color images based on local gamma correction. The proposed variational model contains an energy functional to determine a local gamma function such that the gamma values can be set according to the local information of the input image. A spatial regularization of the gamma function is incorporated into the functional so that the contrast in an image can be modified by using the information of each pixel and its neighboring pixels. Another regularization term is also employed to preserve the ordering of pixel values. Theoretically, the existence and uniqueness of the minimizer of the proposed model are established. A fast algorithm can be developed to solve the resulting minimization model. Experimental results on benchmark images are presented to show that the performance of the proposed model are better than that of the other testing methods.

An alternative multiscale framework for variational perceptually-inspired contrast enhancement of color images

An alternative multiscale framework for variational perceptually-inspired contrast enhancement of color images

Color Image Contrast Enhancement Based on Spatial Transformation and Expert Fuzzy Technique

The current image enhancement algorithms have the problems of color drift, difficulty of parameter selection, convergence guarantee, large amount of computation and poor real-time performance. The effect of image enhancement should mainly consider the visual perception of human eyes, and it is difficult to process images with accurate standards and methods. Therefore, the fuzzy technology based on imprecise concepts and expert experience is a favourable approach to solve the problems of current image enhancement algorithm. This paper proposed a color image contrast enhancement technique, firstly to hue, saturation and intensity of color space from RGB color space transform, then image enhancement processing would be implemented to the intensity component of the color image by using expert fuzzy technology. The proposed algorithm does not change the hue and saturation of the original color image. The contrast experiment shows that the proposed method has improved the visual effect of the image obviously. The amount of calculation is small and the real-time performance is well.

Automatic color image contrast enhancement using Gaussian mixture modeling, piecewise linear transformation, and monotone piecewise cubic interpolant

Conventional contrast enhancement algorithms use complicated nonlinear mapping functions with parameters specified manually. In this paper, a parameter-free piecewise linear transformation and monotone piecewise cubic interpolation are used for color image contrast enhancement. The Gaussian mixture model is used to divide luminance histogram of input color image into multiple sub-histograms. Each sub-histogram is mapped linearly or nonlinearly to a portion of available output dynamic range proportional to its extent and the number of its pixels. The proposed algorithm has more controlled contrast enhancement than conventional algorithms to preserve natural outlook and local details in the input color image. This is done by considering brightness histogram preservation of input image by proposed algorithm, due to considering extent of each sub-histogram for allocating a portion of output dynamic range to it. Experimental results show that the proposed algorithm produces better or comparable contrast-enhanced images with natural outlook than several conventional algorithms on various types of images.

컬러 영상의 채널 간 상관관계를 고려한 콘트라스트 및 채도 동시 향상 알고리즘

컬러 영상의 콘트라스트를 향상시키기 위해서 가장 많이 사용되는 방법은 컬러 영상의 밝기 값에 콘트라스트 향상 알고리즘을 적용시키는 것이다. 이 방법은 색상 열화 없이 콘트라스트가 향상된 결과를 얻을 수 있지만, 원본 영상 대비 결과 영상의 채도가 감소되는 문제가 발생한다. 컬러 영상의 콘트라스트를 향상시키기 위한 또 다른 방법은 컬러 영상의 각 채널에 콘트라스트 향상 알고리즘을 적용시키는 것이다. 이 방법은 콘트라스트와 채도가 동시에 향상되지만 색상 열화가 발생하는 단점이 있다. 본 논문에서는 컬러 영상의 각 채널 처리 시 발생하는 색상 열화 원인을 분석하여 이를 보상해주는 방법으로 색상 열화문제를 해결하였다. 또한 각 채널의 특성을 고려한 채널 적응적 콘트라스트 향상 방법을 이용하여 색상 열화를 방지하는 방법을 제안하였다. 제안하는 방법을 이용하면 컬러 영상의 콘트라스트 향상뿐만 아니라 색상 열화가 발생하지 않으면서 채도가 향상된 결과 영상을 획득할 수 있다. 실험 결과를 통해 제안하는 방법이 주관적 평가뿐 아니라 객관적 평가 지표들에서도 기존 방법들보다 우수한 성능을 보이는 것을 확인 할 수 있다.

A Novel Enhancement Algorithm Combined with Improved Fuzzy Set Theory for Low Illumination Images

A novel enhancement method of global brightness modulation and local contrast enhancement combined with the improved fuzzy set theory is proposed for color image contrast enhancement. The proposed method consists of three stages. Firstly, putting forward nonlinear global brightness mapping model adjusts dynamic range of images for luminance componentVofHSVcolor space. Secondly, membership function is established in stages to adjust local contrast of image details nonlinearly based on fuzzy set theory. Finally, the enhanced images are transformed fromHSVcolor space intoRGBcolor space. The experiments further show that the proposed method has the shortest processing time, the highest AIC values, and the least NIQE values among the other four conventional methods. It has excellent effect, which can enhance the global brightness and local contrast, and advance visibility of low illumination images.

Automatic Method for Contrast Enhancement of Natural Color Images

The contrast enhancement is great challenge in the image processing when images are suffering from poor contrast problem. Therefore, in order to overcome this problem an automatic method is proposed for contrast enhancement of natural color images. The proposed method consist of two stages: in first stage lightness component in YIQ color space is normalized by sigmoid function after the adaptive histogram equalization is applied on Y component and in second stage automatic color contrast enhancement algorithm is applied on output of the first stage. The proposed algorithm is tested on different NASA color images, hyperspectral color images and other types of natural color images. The performance of proposed algorithm is evaluated and compared with the other existing contrast enhancement algorithms in terms of colorfulness metric and color enhancement factor. The higher values of colorfulness metric and color enhancement factor imply that the visual quality of the enhanced image is good. Simulation results demonstrate that proposed algorithm provides higher values of colorfulness metric and color enhancement factor as compared to other existing contrast enhancement algorithms. The proposed algorithm also provides better visual enhancement results as compared with the other existing contrast enhancement algorithms.

Parametric based morphological transformation for contrast enhancement of color images in poor-lighting

The objective of contrast operators consists in normalizing the gray levels of the input image for the purpose of avoiding abrupt changes in intensity among different regions. In this paper morphological transformations are used to detect the background in color images characterized by poor lighting. The disadvantage of contrast enhancement as studied in previous contrast enhancement algorithms is over illumination. An efficient algorithm is introduced to tackle the problem of over illumination by controlling the intensities at dark and bright regions of an image and preserve the geometry of the object. Finally the performance of the proposed algorithm is illustrated through the processing of gray scale images and color images with different backgrounds.

CONTRAST ENHANCEMENT OF COLOR IMAGES USING IMPROVED RETINEX METHOD

Color images provide large information for human visual perception compared to grayscale images. Color image enhancement methods enhance the visual data to increase the clarity of the color image. It increases human perception of information. Different color image contrast enhancement methods are used to increase the contrast of the color images. The Retinex algorithms enhance the color images similar to the scene perceived by the human eye. Multiscale retinex with color restoration (MSRCR) is a type of retinex algorithm. The MSRCR algorithm results in graying out and halo artifacts at the edges of the images. So here the focus is on improving the MSRCR algorithm by combining it with contrast limited adaptive histogram equalization (CLAHE) using image.