Image Contrast Enhancement Algorithm Research Articles

Contrast enhancement algorithm for infrared images based on atmospheric scattering model

With the rapid development of infrared technology, infrared cameras are widely used in the military, medical, and industrial fields. However, the thermal radiation information received by infrared cameras often undergoes degradation due to random factors such as scattering or reflection during transmission. Consequently, the final infrared image suffers from poor contrast and blurred vision. To address these problems, this paper proposes an infrared image contrast enhancement algorithm based on the atmospheric scattering model. Firstly, we modify the hypothesis of the dark channel prior theory, and the pseudo dark channel prior theory is deduced to meet the processing requirements of single-channel images. Secondly, we propose anti-target interference and filtering threshold method to accurately estimate the atmospheric illumination value and transmittance of the atmospheric scattering model. These methods effectively mitigate the influence of infrared targets on the atmospheric illumination value and eliminate the blocking artifact effect caused by pixel value jumps in the transmittance images. Furthermore, we employ adaptive histogram equalization with contrast limitation, highlighting the global contrast. This paper conducts experiments on the M3FD dataset and employs objective evaluations using Visible Edge (E), Figure Definition (FD), Peak Signal-to-Noise Ratio (PSNR), Information Entropy (IE), Structure Similarity Index Measure (SSIM), and Visual Information Fidelity (VIF). The results indicate that the proposed algorithm enhances the overall contrast of images while highlighting fine texture details, exhibiting good visual effects. Both subjective and objective evaluations outperform seven other contrast algorithms, demonstrating the effectiveness of the proposed method.

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.

An efficient multi-level pre-processing algorithm for the enhancement of dermoscopy images in melanoma detection.

In this paper, a multi-level algorithm for pre-processing of dermoscopy images is proposed, which helps in improving the quality of the raw images, making it suitable for skin lesion detection. This multi-level pre-processing method has a positive impact on automated skin lesion segmentation using Regularized Extreme Learning Machine. Raw images are subjected to de-noising, illumination correction, contrast enhancement, sharpening, reflection removal, and virtual shaving before the skin lesion segmentation. The Non-Local Means (NLM) filter with lowest Blind Reference less Image Spatial Quality Evaluator (BRISQUE) score exhibits better de-noising of dermoscopy images. To suppress uneven illumination, gamma correction is subjected to the denoised image. The Robust Image Contrast Enhancement (RICE) algorithm is used for contrast enhancement, and produces enhanced images with better structural preservation and negligible loss of information. Unsharp masking for sharpening exhibits low BRISQUE scores for better sharpening of fine details in an image. Output images produced by the phase congruency-based method in virtual shaving show high similarity with ground truth images as the hair is removed completely from the input images. Obtained scores at each stage of pre-processing framework show that the performance is superior compared to all the existing methods, both qualitatively and quantitatively, in terms of uniform contrast, preservation of information content, removal of undesired information, and elimination of artifacts in melanoma images. The output of the proposed system is assessed qualitatively and quantitatively with and without pre-processing of dermoscopy images. From the overall evaluation results, it is found that the segmentation of skin lesion is more efficient using Regularized Extreme Learning Machine if the multi-level pre-processing steps are used in proper sequence.

A novel detail weighted histogram equalization method for brightness preserving image enhancement based on partial statistic and global mapping model

Histogram equalization (HE) is a classic and widely used image contrast enhancement algorithm for its good performance and high efficiency. However, over-enhancement caused by high peak in the histogram affects the subjective quality of the image processed by HE to a large extent. In this paper, a detail weighted histogram equalization (DWHE) method is proposed based on a novel histogram modification (HM) model named partial statistic and global mapping (PSGM) to alleviate high peak and suppress over-enhancement. Moreover, the authors implement a refined version of gamma correction (GC) named texture enhancement function (TEF) on high-frequency images to reduce the noise amplification effect. At last, the authors propose a novel adaptively weighted pixel-level image fusion method to further reduce the phenomenon of over-enhancement and improve brightness distribution. Both subjective and quantitative evaluations are conducted on images containing a variety of scenes. Compared with several state-of-the-art image enhancement methods, the proposed framework obtained generally the best performance in aspects of both subjective appearance and objective evaluation indices. Therefore, it is proved that the proposed methods can effectively alleviate the over-enhancement, enrich image details, and efficiently improve the visual quality while preserving the brightness of the image.

Image contrast enhancement algorithm for X-ray observation of space materials in situ

Chinese Space Station has planned a high-temperature material science experiment rack, equipped with an X-ray projection imaging module, to support the development of material experiments and research in space. BiFeO3 has been selected as the first batch of experimental materials for Chinese Space Station. The melting and solidification process of BiFeO3, an opaque, high-temperature material, is observed by X-ray observation module in situ. X-ray is the dominant way to observe opaque materials due to its penetrability. In-situ observation of materials is the top priority of this study, so we have strict requirements on image quality, and high-quality images can better analyze the properties and properties of materials. Limited by narrow size and high temperature conditions, the X-ray images collected have low contrast, serious noise pollution, and poor imaging quality. To enhance the contrast and improve the edge details of such images, a grayscale weighted histogram equalization combined with high-frequency enhancement (GWHE-HFE) algorithm is proposed. First, we add a mask to the input image to obtain the region of interest (ROI), and then filter out the low-frequency components of the image by Gaussian high-pass filter to preserve high-frequency details. Second, the image obtained in the previous and the X-ray image of ROI are respectively multiplied by a coefficient and added to obtain the edge-emphasized X-ray image. And then, we use grayscale weighted histogram equalization (GWHE) to process the image obtained in the second step to obtain the contrast enhanced X-ray image. The enhanced image shows the crystal grains and the thin bands where the solid and the melt intersect, and it is helpful to accurately locate the solid solution interface. Experiments on X-ray images of BiFeO3 growth demonstrate that this combined method outperforms existing ones both qualitatively and quantitatively, providing an in-depth and effective analysis method for in high-temperature material-science experiments.

Dehazing buried tissues in retinal fundus images using a multiple radiance pre-processing with deep learning based multiple feature-fusion

Fundus is the inner part of the eye that is noticed while viewing through the pupil during an eye check-up. It’s made up of the retina, the macula and the optic disk. Fundus images are used to inspect and track the progression of disease-related abnormalities. Medical background images taken using high-resolution cameras lack transparency, sharpness, and an optimum level of brightness and contrast. These are critical factors that define the extent at which human experts or automated devices based on artificial intelligence can effectively distinguish between healthy and abnormal tissues. Current work describes a two-stage pre-processing image with deep learning based residual features and dynamic gradient features for enhancing retinal fundus images. In the first stage, a sequence of enhanced images with varying radiance is generated using an image brightening, contrast enhancement, and color restoration algorithm. This stage provides a sufficient improvement in contrast, sharpness and color restoration compared to the original image. But it’s also correlated with a high level of noise. From the visual results, this amount of noise can be detected. It will also decrease the Peak Signal To Noise Ratio (PSNR). Thus the second stage, which serves as a filter which combines the power of the proposed Multiple Feature-Fusion-based Deep Learning Network and the exposures extracted from the first stage image series. The technique is demonstrated in the following sections and tested in depth using a standard data set. Visual and quantitative experiments also indicate that the new approach is also capable of achieving superior performance over existing image dehazing techniques.

MH-COVIDNet: Diagnosis of COVID-19 using deep neural networks and meta-heuristic-based feature selection on X-ray images

COVID-19 is a disease that causes symptoms in the lungs and causes deaths around the world. Studies are ongoing for the diagnosis and treatment of this disease, which is defined as a pandemic. Early diagnosis of this disease is important for human life. This process is progressing rapidly with diagnostic studies based on deep learning. Therefore, to contribute to this field, a deep learning-based approach that can be used for early diagnosis of the disease is proposed in our study. In this approach, a data set consisting of 3 classes of COVID19, normal and pneumonia lung X-ray images was created, with each class containing 364 images. Pre-processing was performed using the image contrast enhancement algorithm on the prepared data set and a new data set was obtained. Feature extraction was completed from this data set with deep learning models such as AlexNet, VGG19, GoogleNet, and ResNet. For the selection of the best potential features, two metaheuristic algorithms of binary particle swarm optimization and binary gray wolf optimization were used. After combining the features obtained in the feature selection of the enhancement data set, they were classified using SVM. The overall accuracy of the proposed approach was obtained as 99.38%. The results obtained by verification with two different metaheuristic algorithms proved that the approach we propose can help experts during COVID-19 diagnostic studies.

Retinal Image Enhancement using Ordering Gap Adjustment and Brightness Specification

Color retinal image enhancement plays an important role in improving an image quality suited for reliable diagnosis. For this problem domain, a simple and effective algorithm for image contrast and color balance enhancement namely Ordering Gap Adjustment and Brightness Specification (OGABS) was proposed. The OGABS algorithm first constructs a specified histogram by adjusting the gap of the input image histogram ordering by its probability density function under gap limiter and Hubbard’s dynamic range specifications. Then, the specified histograms are targets to redistribute the intensity values of the input image based on histogram matching. Finally, color balance is improved by specifying the image brightness based on Hubbard’s brightness specification. The OGABS algorithm is implemented by the MATLAB program and the performance of our algorithm has been evaluated against data from STARE and DiaretDB0 datasets. The results obtained show that our algorithm enhances the image contrast and creates a good color balance in a pleasing natural appearance with a standard color of lesions.

Colonoscopy contrast-enhanced by intuitionistic fuzzy soft sets for polyp cancer localization

Medical images often suffer from low contrast, irregular gray-level spacing and contain a lot of uncertainties due to constraints of imaging devices and environment (various lighting conditions) when capturing images. In order to achieve any clinical-diagnosis method for medical imaging with better comprehensibility, image contrast enhancement algorithms would be appropriate to improve the visual quality of medical images. In this paper, an automated image enhancement method is presented for colonoscopy images based on the intuitionistic fuzzy soft set. The fuzzy soft set is used to model the intuitionistic fuzzy soft image matrix based on a set of soft features of the colonoscopy images. The technique decomposes the fuzzy image into multiple blocks and estimates a soft-score based on an adaptive soft parametric hesitancy map by using the hesitant entropy for each block to quantify the uncertainties. In the processing stage, an adaptive intensity modification process is done for each block according to its soft-score. These scores are accurately addressed the gray-level ambiguities in colonoscopy images that lead to better results. Finally, the enhanced image achieved by performing a defuzzification together with all unprocessed blocks. Qualitative and quantitative assessments demonstrate that the proposed method improves image contrast and region-of-interest of polyps in colonogram. Experimental results on enhancing a large CVC-Clinic-DB and ASU-Mayo clinic colonoscopy benchmark datasets show that the proposed method outperforms the state-of-the-art medical image enhancement methods.

Real-time Image Contrast Enhancement VLSI Design for Intelligent Autonomous Vehicles

Abstract A novel hardware-oriented image contrast enhancement algorithm is proposed in this study for intelligent autonomous vehicles. It utilizes a weighted filter and calculates the brightness values of an image based on the adjusted image. The brightness values are processed to either reduce or increase the brightness values of the points. To further improve the quality of an image, the algorithm implements a block-based pixel processing as opposed to a per image frame processing. The brightness values for each block or area in the image are used to improve the contrast of the image. This is accomplished by reducing or increasing the different brightness values of the pixel or lifting point in each block. Simulation results showed that compared with previously proposed algorithms, this work improved on the average discrete entropy by 1% and increased the average color enhancement factor by 8.5%. The proposed novel algorithm was realized using TSMC 0.18 μm CMOS cell process. The VLSI design has a total gate count of 6028 and operates with a frequency of 201 MHz and a power rating of 17.47 mW.

A novel enhanced cuckoo search algorithm for contrast enhancement of gray scale images

A good contrast image has a significant role in different image processing applications and computer vision algorithms. One of the most common contrast enhancement approaches is histogram equalization (HE) that enhances the contrast of an image globally. However, it gives rise to some over-enhanced regions, loss of detail information, and enhancement of noise. In order to improve the performance of the HE algorithm, local HE and adaptive HE algorithms have been proposed but with limited success. Recently, an evolutionary algorithm named cuckoo search (CS) algorithm has been employed for automatic image contrast enhancement showing promising performance. In this paper, we propose a novel enhanced cuckoo search (ECS) algorithm for image contrast enhancement. In addition, we propose a new range of search space for the parameters of the local/global enhancement (LGE) transformation that need to be optimized. The proposed ECS algorithm is applied to several low contrast test images and its performance is compared with that of the CS algorithm. Next, we compare the performance of the ECS algorithm with artificial bee colony algorithm using the proposed LGE transformation and a global transformation. In the last stage of performance evaluation, the ECS algorithm is compared with several image enhancement algorithms, namely, HE, CLAHE, Particle Swarm Optimization (PSO), CS, modified CS and CS-PSO algorithms. In all cases, we have shown the superiority of the ECS algorithm in terms of several performance measures.

Modified Local and Global Contrast Enhancement Algorithm for Color Satellite Image

The quality of remotely sensed satellite images depends on the reflected electromagnetic radiation from the earth’s surface features. Lack of consistent and similar amounts of energy reflected by different features from the earth’s surface results in a poor contrast satellite image. Image enhancement is the image processing of improving the quality that the results are more suitable for display or further image analysis. In this paper, we present a detailed model for color image enhancement using the quaternion framework. We introduce a novel quaternionic frequency enhancement algorithm that can combine the color channels and the local and global image processing. The basic idea is to apply the α-rooting image enhancement approach for different image blocks. For this purpose, we split image in moving windows on disjoint blocks. The parameter alfa for every block and the weights for every local and global enhanced image driven through optimization of measure of enhancement (EMEC). Some presented experimental results illustrate the performance of the proposed approach on color satellite images in comparison with the state-of-the-art methods.

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.

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.

Chebyshev-based technique for automated restoration of digital copies of faded photographic prints

We present a technique for automated restoration of digital images obtained from faded photographic prints. The proposed defading technique uses our early proposed image contrast enhancement algorithm based on a contrast measure of images in the Chebyshev moment transform domain. Obtained experimental results demonstrate some advantages of the technique as compared to other widely used image enhancement methods.

Microarray Image Analysis Using Genetic Algorithm

<p>Microarray technology allows the simultaneous monitoring of thousands of genes. Based on the gene expression measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Gridding, segmentation and intensity extraction are the three important steps in microarray image analysis. This paper presents microarray image analysis using Genetic Algorithm. A new algorithm for microarray image contrast enhancement is presented using Genetic Algorithm. Contrast enhancement is crucial step in extracting edge information in image and finally this edge information is used in gridding of microarray image. Mostly segmentation of microarray image is carried out using clustering algorithms. Clustering algorithms have an advantage that they are not restricted to a particular shape and size for the spots. In this paper, segmentation using Genetic Algorithm by optimizing K-means index and Jm measure is presented. The qualitative analysis shows that the proposed method achieves better segmentation results than K-means and FCM algorithms.</p>

A General Framework for 1-D Histogram-baesd Image Contrast Enhancement

In this paper, a general framework for image contrast enhancement algorithm based on an optimization problem is presented. Through this optimization, the intensities can be better distributed. The algorithm is based on the facts that the histogram of the enhanced image is close to the input image histogram and uniform distribution, simultaneously. Based on this fact, we obtain a closed form optimal solution for the histogram of the enhanced image. Experimental results in a wide range of images demonstrate the high-performance of the proposed method.

Underwater image de-scattering and classification by deep neural network

Vision-based underwater navigation and object detection requires robust computer vision algorithms to operate in turbid water. Many conventional methods aimed at improving visibility in low turbid water. High turbid underwater image enhancement is still an opening issue. Meanwhile, we find that the de-scattering and color correction of underwater images affect classification results. In this paper, we correspondingly propose a novel joint guidance image de-scattering and physical spectral characteristics-based color correction method to enhance high turbidity underwater images. The proposed enhancement method removes the scatter and preserves colors. In addition, as a rule to compare the performance of different image enhancement algorithms, a more comprehensive image quality assessment index Qu is proposed. The index combines the benefits of SSIM index and color distance index. We also use different machine learning methods for classification, such as support vector machine, convolutional neural network. Experimental results show that the proposed approach statistically outperforms state-of-the-art general purpose underwater image contrast enhancement algorithms. The experiment also demonstrated that the proposed method performs well for image classification.

An Efficient Image Contrast Enhancement Algorithm Using Genetic Algorithm and Fuzzy Intensification Operator

Image contrast enhancement algorithms play a crucial role in image processing and computer vision. The main challenge in contrast enhancement is that an algorithm suitable for low contrast distorted images does not suit for high contrast distorted images. In this paper, an efficient contrast enhancement algorithm with automated parameterization is proposed using the concept of genetic algorithm and fuzzy intensification operator. Main focus of the proposed method is to improve the visibility information of an image by manipulating their intensity information. Simulation results of the proposed fuzzy-genetic based method were compared with standard existing methods such as log, gamma, linear contrast stretching, histogram equalization, adaptive histogram equalization and rule based fuzzy method using their default parameter values. Performance of the proposed and existing methods on very low, low, moderate, high and very high levels of contrast distorted images were quantitatively measured using peak signal to noise ratio (PSNR), structural similarity index measure (SSIM) and feature similarity index measure (FSIM). The PSNR, SSIM and FSIM values were statistically analysed by two-way ANOVA. Results of this experiment inferred that (a) the contrast enhancement techniques performed well when the level of distortions were very low to moderate, (b) contrast enhancement was better in the proposed fuzzy-genetic based method than other existing methods, and (c) overall, the proposed fuzzy-genetic based method performed well on very low to very high levels of distorted images with higher PSNR, SSIM and FSIM values than other existing methods.

Design and Implementation of Gain-Offset Correction Algorithm Hardware Architecture for Grayscale and Color Images Contrast Enhancement

This paper describes the design and implementation of a novel, high-speed hardware (HW) architecture for the gain-offset correction (GOC) image contrast enhancement algorithm on an FPGA fabric. The design is extremely fast and has been shown to process megapixel image frames at frame rates greatly exceeding real-time requirements. The design is small and compact enough to fit on small FPGAs to form a low-cost image processing solution. The automated nature of the contrast enhancement operation is due to the computation of global image statistics for each image. The architecture does not store any image frames to perform this task, heavily reducing memory requirements.