Adaptive Contrast Enhancement Algorithm Research Articles

Research on the Contrast Enhancement Algorithm for X-ray Images of BiFeO3 Material Experiment

High-Temperature Materials Science Experiment Cabinet on the Chinese Space Station is mainly used to carry out experimental research related to high-temperature materials science in microgravity. It is equipped with an X-ray transmission imaging module, which is applied to realize transmission imaging of material samples under microgravity. However, the X-ray light source is far away from the experimental samples, and the images obtained by the module are blurred, so it is impossible to accurately observe the morphological changes during the melting and solidification processes of high-temperature materials. To address this issue, this paper proposed a contrast enhancement algorithm specifically designed for X-ray images obtained during the experiments of high-temperature materials. The algorithm is based on gradient three-interval equalization, and it is combined with a Gaussian function to expand the gradient histogram. Meanwhile, the local gray level information within each gradient interval is corrected by designing an improved adaptive contrast enhancement algorithm. By comparing with Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithms, EnlightenGAN, and Wavelet algorithms, the Contrast Enhancement based contrast-changed Image Quality measure (CEIQ) and Measure of Enhancement (EME) are improved by an average of 56.97%, 10.58%, and Measure of Entropy (MOE) are improved by an average of 7.74 times. The experimental results show that the algorithm makes the image details clearer on the basis of image contrast enhancement. The solid-liquid interface in the image can be clearly observed after contrast enhancement. The algorithm provides strong support for the study of interface dynamics during the experiment process of high-temperature materials.

MRI brain tumor detection using deep learning and machine learning approaches

The development of aberrant brain cells, some of which may become cancerous, is known as a brain tumour. The quality of life and life expectancy of patients are enhanced by early and timely illness identification and treatment plans. Magnetic Resonance Imaging (MRI) scans are the most common approach for finding brain tumors. However, the ability of radiologists and other clinical experts to identify, segment, and remove contaminated tumour regions from MRI images is a critical factor in a process that is iterative and labor-intensive and relies on those individuals' abilities in these areas. Concepts for image processing may envision the diverse human organ anatomical structures. It is difficult to find abnormal brain regions using simple imaging methods. Over the last several years, interest in the emerging machine learning field of “Deep Learning (DL)" has grown significantly. It was extensively used in numerous applications and shown to be an effective Machine Learning (ML) technique for many of the challenging issues. This research suggests a novel MRI brain tumour detection method based on DL and ML. Initially the MRI images are collected and preprocessed using Adaptive Contrast Enhancement Algorithm (ACEA) and median filter. Fuzzy c-means based segmentation is done to segment the preprocessed images. The features like energy, mean, entropy and contrast are extracted using Gray-level co-occurrence matrix (GLCM). The abnormal tissues are classified using the proposed Ensemble Deep Neural Support Vector Machine (EDN-SVM) classifier. The numerical findings reveal a better accuracy (97.93 %), sensitivity (92 %), and specificity (98 %) in recognizing aberrant and normal tissue from brain MRIimages, which supports the effectiveness of the approach that was recommended.

PREVENTION OF RODENT ATTACK ON TERTIARY PACKAGE AT STORAGE LEVEL

This proposes to use some image processing methods as a data normalization method for machine learning. Conventionally, z-score normalization is widely used for pre-processing of data. In the proposed approach, in addition to z-score normalization, a number of histogram-based image processing methods such as histogram equalization are applied to training data and test data as a pre-processing method for machine learning. We evaluate the effectiveness of the proposed approach by using a support vector machine algorithm and a random forest one. In experiments, the proposed scheme is applied to a face-based authentication algorithm with SVM/random forest classifiers to confirm the effectiveness. For SVM classifiers, both z- score normalization and image enhancement work well as a pre-processing method for improving the accuracy. In contrast, for random forest classifiers, a number of image enhancement methods work well, although z- score normalization is unusual for improving the accuracy. The proposed pre-processing method focuses on leveraging histogram analysis to address challenges related to contrast variations, colour distributions, and noise levels in diverse image datasets. The key objectives include the development of an adaptive contrast enhancement algorithm, colour normalization techniques, and strategies for noise reduction.

Image enhancement using an improved adaptive contrast enhancement algorithm in neutron radiography

As a non-destructive testing technique, neutron radiography has received increasing attention in scientific research. However, the inherent limitations of neutron images, such as insufficient resolution and contrast, have been attributed to the low conversion efficiency of the neutron conversion screen and the low-light quantum efficiency of the charge-coupled device camera. This paper presents a novel neutron image enhancement method that utilizes the sparrow search algorithm to automatically optimize the gain factor coefficient of the adaptive contrast enhancement algorithm and incorporates neutron image information to achieve image enhancement. Experimental results show that compared with other enhancement methods, the proposed method can effectively improve the quality of neutron images visually and numerically and has good operational stability.

A machine vision method for measurement of machining tool wear

Tool wears directly affect the quality of product and service life of tool. This paper proposes a machine vision-based measurement method for chisel edge wear of drills. Firstly, the full contour of a drill is extracted by local variance threshold segmentation. Secondly, the image is enhanced by using an adaptive contrast enhancement algorithm based on bidimensional local mean decomposition (BLMD). A threshold segmentation method is proposed to extract contour of the non-worn area. After the above two contours are superimposed, the centroid of each region in the binary image is calculated as the starting point to fill in the overflow water for tool wear extraction. Finally, the chisel edge wear can be measured directly by counting the number of pixels. The experiment in the process of drilling is performed to verify the effectiveness of the proposed method. The experimental results show that the proposed method effectively implements the measurement of tool wear.

Color correction and adaptive contrast enhancement for underwater image enhancement

To address the color cast and low contrast of underwater images, we propose a color correction and adaptive contrast enhancement algorithm for underwater image enhancement. In our work, we first design the dedicated fractions to compensate the lower color channels, which are calculated by considering the ratio of the differences between the upper and lower color channels to the lower color channels. Subsequently, an adaptive contrast enhancement algorithm is applied to each color channel is proposed to produce the background-stretched and foreground-stretched images, the combination of these two images significantly improves the contrast of the output image. Finally, a unsharp masking technique is applied to sharpen the overall image. Experiments on the standard underwater image enhancement benchmark dataset have proven that our algorithm can output high-quality images, while the performer of our algorithm is at least 5% better than the second-best in the average of all metric scores.

An efficient demodulation scheme for VLC signal image processing

With the development of visible light communication (VLC) and widespread applications of smart terminals with complementary- metal-oxide-semiconductor (CMOS) image sensor, researchers pay more and more attention in VLC systems with CMOS-based receiver. The blooming effect and light noise make the threshold decision in image processing difficult, which deteriorates the system performance a lot. In this paper, an efficient demodulation scheme is proposed to improve the performance of image processing. In the proposed demodulation scheme, a region of interest (ROI) based column matrix selection scheme is employed to avoid blooming effect, and an adaptive contrast enhancement (ACE) algorithm is designed to increase the contrast of bright and dark stripes. Experimental results show that the proposed scheme especially fits for high fluctuation data pattern. The demodulation with a data rate of 5.76 kbit/s can successfully achieve 7% forward error correction (FEC) limit even when the illuminance is very low.

Global and Adaptive Contrast Enhancement for Low Illumination Gray Images

Aiming at solving the problems of overall darkness, uneven illumination and low contrast of image under low illumination conditions, we present a global and adaptive contrast enhancement algorithm for low illumination gray images in this paper. The proposed algorithm is based on the Bilateral Gamma Adjustment function and combined with the particle swarm optimization (PSO). For the PSO, the gray standard variance is integrated into the evaluation function. To reconcile the dilemma of promoting the gray values of dark areas and suppressing the gray values of local bright areas at the same time, the information of entropy, edge content, and gray standard variance are used as the objective function for each particle to evaluate the gray image enhancement results. Then, the algorithm globally enhances the quality of the image by determining the optimal $\alpha $ value. Meanwhile, the learning factors of the PSO are updated during the iteration of optimization in the proposed algorithm. Compared with histogram equalization (HE), double plateau histogram equalization (DPHE), contrast limited adaptive histogram equalization (CLAHE), linear contrast stretching (LCS), adaptive gamma correction weighting distribution (AGCWD), the traditional PSO and MSF-PSO algorithm, the proposed algorithm significantly enhances the visual effect of the low illumination gray images. The experimental results demonstrate the superior capabilities of the proposed algorithm in enhancing the contrast of the image, such as improving the overall visual effect of the low illumination gray image and avoiding over-enhancement in the local area (s).

An Efficient Adaptive Algorithm for Electron Microscopic Image Enhancement and Feature Extraction

In this article, a block-based adaptive contrast enhancement algorithm has been proposed, which uses a modified sigmoid function for the enhancement and features extraction of electron microscopic images. The algorithm is based on a modified sigmoid function that adapts according to the input microscopic image statistics. For feature extraction, the contrast of the image is very important and authentic property by which this article enhances the visual quality of the image. In this work, for better contrast enhancement of image, a block based on input value, combined with a modified sigmoid function that is used as contrast enhancer provides better EMF values for a smaller block size. It provides localized contrast enhancement effects adaptively which is not possible using other existing techniques. Simulation and experimental results demonstrate that the proposed technique gives better results compared to other existing techniques when applied to electron microscopic images. After the enhancement of microscopic images of actinomycetes, various important features are shown, like coil or spiral, long filament, spore and rod shape structures. The proposed algorithm works efficiently for different dark and bright microscopic images.

Shape Defect Detection using Local Standard Deviation and Rule-Based Classifier for Bottle Quality Inspection

This paper presents shape analysis using Local Standard Deviation (LSD) technique to detect shape defect of the bottle for product quality inspection. The proposed analysis framework includes segmentation, feature extraction, and classification. The shape of the bottle was segmented using LSD technique in order to obtain higher enhancement at the low contrast area and low enhancement at the high contrast area. <span lang="EN-MY">The contrast gain that was applied in Adaptive Contrast Enhancement (ACE) algorithm, was presented inversely proportional to LSD in order to detect and eliminate background noise at the bottle edge. After the segmentation process, the parameters of the bottle shape such as height, width, area, and extent were extracted and applied in classification stage. The rule-based classifier was used to classify the shape of the bottle either good or defect. The offline experimental results exhibit superior segmentation on performance with 100% accuracy for 100 sample images. This shows that the LSD could be an effective technique to monitor the product quality.</span>

Contrast Enhancement Using Combined 1-D and 2-D Histogram-Based Techniques

This letter presents an adaptive contrast enhancement algorithm considering both preservation of the shape of a one-dimensional (1-D) histogram and statistical information on the gray-level differences between neighboring pixels obtained by a 2-D histogram. The proposed system consists of two modules. One is to enhance the entire contrast by stretching the 1-D histogram while preserving the shape of the histogram. The other is to improve the details of nonsmooth areas occurring frequently in input images. These are formulated into a single constrained optimization problem. Compared with several state-of-the-art enhancement algorithms, the proposed algorithm shows highly competitive performance.

Real-time implementation of an adaptive simultaneous dynamic range compression and local contrast enhancement algorithm on a GPU

Dynamic range compression is a fundamental function required in digital video cameras and display monitors to improve the visual appeal of color images. This paper presents a real-time implementation of an algorithm for adaptive dynamic range compression and contrast enhancement on a graphics processing unit (GPU). To achieve this, we first designed an adaptive intensity transfer function to handle the enhancement of standard-dynamic-range (8 bits/channel) images with both low- and high-intensity. The proposed algorithm then combines the intensity transfer function with an existing simultaneous dynamic range compression and local contrast enhancement (SDRCLCE) algorithm for the simultaneous compression of dynamic range and enhancement of local contrast. To reach real-time performance in processing high-resolution color images, the proposed algorithm is implemented using an NVIDIA GeForce GTX 650 Ti GPU based on NVIDIA’s Compute Unified Device Architecture (CUDA) parallel programming running on a 3.33 GHz Intel Core i5-661 CPU. Compared with a LUT-accelerated implementation, the proposed GPU implementation was shown to accelerate the processing of 1024 × 1024 color images by 11.1 times and color images of 4096 × 4096 pixels by 7.5 times, including the cost of memory copy between the host and device.

Adaptive image contrast enhancement algorithm for point-based rendering

Surgical simulation is a major application in computer graphics and virtual reality, and most of the existing work indicates that interactive real-time cutting simulation of soft tissue is a fundamental but challenging research problem in virtual surgery simulation systems. More specifically, it is difficult to achieve a fast enough graphic update rate (at least 30 Hz) on commodity PC hardware by utilizing traditional triangle-based rendering algorithms. In recent years, point-based rendering (PBR) has been shown to offer the potential to outperform the traditional triangle-based rendering in speed when it is applied to highly complex soft tissue cutting models. Nevertheless, the PBR algorithms are still limited in visual quality due to inherent contrast distortion. We propose an adaptive image contrast enhancement algorithm as a postprocessing module for PBR, providing high visual rendering quality as well as acceptable rendering efficiency. Our approach is based on a perceptible image quality technique with automatic parameter selection, resulting in a visual quality comparable to existing conven- tional PBR algorithms. Experimental results show that our adaptive image contrast enhancement algorithm produces encouraging results both visually and numerically compared to representative algorithms, and experi- ments conducted on the latest hardware demonstrate that the proposed PBR framework with the postprocessing module is superior to the conventional PBR algorithm and that the proposed contrast enhancement algorithm can be utilized in (or compatible with) various variants of the conventional PBR algorithm. © 2015 SPIE and IS&T (DOI: 10.1117/1.JEI.24.2.023033)

An Adaptive Fuzzy Contrast Enhancement Algorithm with Details Preserving

This paper modifies the Adaptive Contrast Enhancement Algorithm with Details Preserving (ACEDP) technique by integrating a fuzzy element in the image type selection. The proposed technique, named the Adaptive Fuzzy Contrast Enhancement with Details Preserving (AFCEDP) technique, first computes the degree of membership of the input image to three categories, i.e. low-, middle- or high-level images. The AFCEDP technique then clips the histogram at different plateau limits that are computed from both the degree of membership and the clipping functions. The classification of an image in the ACEDP technique is done based solely on the intensity range of the maximum number of pixels, which may be inaccurate. In the proposed AFCEDP technique, the image type classification is handled in a better way with the integration of a fuzzy element. The performance of the proposed AFCEDP technique was compared with the conventional ACEDP technique and several state-of-art techniques described in the literature. The simulation results revealed that the AFCEDP technique demonstrates good capability in contrast enhancement and detail preservation. In addition, the experiments using cervical cell images and HEp-2 cell images showed great potential of the AFCEDP technique as a technique for enhancing medical microscopic images.

An Adaptive Contrast Enhancement Algorithm with Details Preserving

An Adaptive Contrast Enhancement Algorithm with Details Preserving

A new adaptive contrast enhancement algorithm for infrared images based on double plateaus histogram equalization

In infrared images, detail pixels are easily immerged in large quantity of low-contrast background pixels. According to these characteristics, an adaptive contrast enhancement algorithm based on double plateaus histogram equalization for infrared images was presented in this paper. Traditional double plateaus histogram equalization algorithm used constant threshold and could not change the threshold value in various scenes, so that its practical usage is limited. In the proposed algorithm, the upper and lower threshold value could be calculated by searching local maximum and predicting minimum gray interval and be updated in real time. With the proposed algorithm, the background of infrared image was constrained while the details could also be enhanced. Experimental results proved that the proposed algorithm can effectively enhance the contrast of infrared images, especially the details of infrared images.

명암비 향상을 위한 서브-히스토그램 면적비 기반의 적응형 히스토그램 재분배 알고리즘

Histogram Equalization (HE) is a very popular technique for enhancing the contrast of an image. HE stretches the dynamic range of an image using the cumulative distribution function of a given input image, therefore improving its contrast. However, HE has a well-known problem : when HE is applied for the contrast enhancement, there is a significant change in brightness. To resolve this problem, we propose An Adaptive Contrast Enhancement Algorithm using Subhistogram Area-Ratioed Histogram Redistribution, a new method that helps reduce excessive contrast enhancement. This proposed algorithm redistributes the dynamic range of an input image using its mean luminance value and the ratio of sub-histogram area. Experimental results show that by this redistribution, the significant change in brightness is reduced effectively and the output image is able to preserve the naturalness of an original image even if it has a poor histogram distribution.

Design of real-time image enhancement preprocessor for CMOS image sensor

This paper presents a design of the real-time digital image enhancement preprocessor for a CMOS image sensor. The CMOS image sensor offers various advantages while it provides lower-quality images than the CCD does. In order to compensate for the physical limitation of the CMOS sensor, a spatially adaptive contrast enhancement algorithm was incorporated into the preprocessor with color interpolation, gamma correction, and automatic exposure control. The efficient hardware architecture for the preprocessor is proposed and was simulated in VHDL. It is composed of about 19 K logic gates, which is suitable for a low-cost one-chip PC camera. The test system was implemented on a FPGA chip in real-time mode, and performed successfully.

Image contrast enhancement based on a histogram transformation of local standard deviation.

The adaptive contrast enhancement (ACE) algorithm, which uses contrast gains (CG's) to adjust the high-frequency components of images, is a well-known technique for medical image processing. Conventionally, the CG is either a constant or inversely proportional to the local standard deviation (ILSD). However, it is known that conventional approaches entail noise overenhancement and ringing artifacts. In this paper, we present a new ACE algorithm that eliminates these problems. First, a mathematical model for the LSD distribution is proposed by extending Hunt's image model. Then, the CG is formulated as a function of the LSD. The function, which is nonlinear, is determined by the transformation between the LSD histogram and a desired LSD distribution. Using our formulation, it can be shown that conventional ACE's use linear functions to compute the new CG's. It is the proposed nonlinear function that produces an adequate CG resulting in little noise overenhancement and fewer ringing artifacts. Finally, simulations using some X-ray images are provided to demonstrate the effectiveness of our new algorithm.

Adaptive Neighborhood Extended Contrast Enhancement and Its Modifications

A new technique for contrast enhancement, namely adaptive neighborhood extended contrast enhancement, and several original modifications on the same are proposed. Developing from the adaptive contrast enhancement algorithm of Beghdadi and Negrate ( Comput. Vision Graphics Image Process. 46, 1989, 162-174), and the adaptive neighborhood histogram equalization algorithm of Paranjape et al. ( CVGIP Graphical Models Image Process. 54(3), 1992, 259-267), the above techniques have been evolved in order to make image enhancement more adaptive and context sensitive. The adaptive neighborhood extended contrast enhancement algorithm, without its other modifications, only combines the features of these two existing algorithms. This algorithm can be used for both image enhancement and de-enhancement and can also be combined with existing procedures such as power variation. To make the algorithm computationally efficient, the computationally efficient adaptive neighborhood extended contrast enhancement procedure is proposed. This modification achieves significant computational speedup without much loss of image quality. The adaptive neighborhood definition extended contrast enhancement procedure is next proposed in order to make the algorithm further adaptive by making its performance independent of its most sensitive parameter. This procedure achieves better identification of different gray level regions by an analysis of the histogram in the locality of every pixel in the image. The experimental results aptly demonstrate the efficacy of the procedure. This technique can be well applied to other contrast enhancement algorithms for improvement of the quality of the enhanced image. Finally, a correction mechanism called repulsion correction is evolved to correct for a specific inability of contrast enhancement algorithms in separating adjacent regions of nearly equal brightness from each other when surrounded by a very large, brighter or darker region.