Contrast Enhancement Algorithm Research Articles

An Adaptive Parameter Optimization Deep Learning Model for Energetic Liquid Vision Recognition Based on Feedback Mechanism.

The precise detection of liquid flow and viscosity is a crucial challenge in industrial processes and environmental monitoring due to the variety of liquid samples and the complex reflective properties of energetic liquids. Traditional methods often struggle to maintain accuracy under such conditions. This study addresses the complexity arising from sample diversity and the reflective properties of energetic liquids by introducing a novel model based on computer vision and deep learning. We propose the DBN-AGS-FLSS, an integrated deep learning model for high-precision, real-time liquid surface pointer detection. The model combines Deep Belief Networks (DBN), Feedback Least-Squares SVM classifiers (FLSS), and Adaptive Genetic Selectors (AGS). Enhanced by bilateral filtering and adaptive contrast enhancement algorithms, the model significantly improves image clarity and detection accuracy. The use of a feedback mechanism for reverse judgment dynamically optimizes model parameters, enhancing system accuracy and robustness. The model achieved an accuracy, precision, F1 score, and recall of 99.37%, 99.36%, 99.16%, and 99.36%, respectively, with an inference speed of only 1.5 ms/frame. Experimental results demonstrate the model's superior performance across various complex detection scenarios, validating its practicality and reliability. This study opens new avenues for industrial applications, especially in real-time monitoring and automated systems, and provides valuable reference for future advancements in computer vision-based detection technologies.

LEFB: A new low-light image contrast enhancement algorithm

Low-light images are challenging for both human observation and computer vision algorithms due to low visibility. To address this issue, various image enhancement techniques such as dehazing, histogram equalization, and neural network-based methods have been proposed. However, most existing methods often suffer from the problems of insufficient contrast and over-enhancement while enhancing the brightness, which not only affects the visual quality of images but also adversely impacts their subsequent analysis and processing. To tackle these problems, this paper proposes a low-light image enhancement method called LEFB. Specifically, the low-light image is first transformed into the LAB color space, and the L channel controlling brightness is enhanced using a local contrast enhancement algorithm. Then, the enhanced image is further enhanced using an exposure fusion-based contrast enhancement algorithm, and finally, a bilateral filtering function is applied to reduce image edge blurriness. Experimental evaluations are conducted on real datasets with four comparison algorithms. The results demonstrate that the proposed method has superior performance in enhancing low-light images, effectively addressing problems of insufficient contrast and over-enhancement, while preserving fine details and texture information, resulting in more natural and realistic enhanced images.

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.

Deep Learning Based Cystoscopy Image Enhancement.

Background: Endoscopy image enhancement technology provides doctors with clearer and more detailed images for observation and diagnosis, allowing doctors to assess lesions more accurately. Unlike most other endoscopy images, cystoscopy images face more complex and diverse image degradation because of their underwater imaging characteristics. Among the various causes of image degradation, the blood haze resulting from bladder mucosal bleeding make the background blurry and unclear, severely affecting diagnostic efficiency, even leading to misjudgment. Materials and Methods: We propose a deep learning-based approach to mitigate the impact of blood haze on cystoscopy images. The approach consists of two parts as follows: a blood haze removal network and a contrast enhancement algorithm. First, we adopt Feature Fusion Attention Network (FFA-Net) and transfer learning in the field of deep learning to remove blood haze from cystoscopy images and introduce perceptual loss to constrain the network for better visual results. Second, we enhance the image contrast by remapping the gray scale of the blood haze-free image and performing weighted fusion of the processed image and the original image. Results: In the blood haze removal stage, the algorithm proposed in this article achieves an average peak signal-to-noise ratio of 29.44 decibels, which is 15% higher than state-of-the-art traditional methods. The average structural similarity and perceptual image patch similarity reach 0.9269 and 0.1146, respectively, both superior to state-of-the-art traditional methods. Besides, our method is the best in keeping color balance after removing the blood haze. In the image enhancement stage, our algorithm enhances the contrast of vessels and tissues while preserving the original colors, expanding the dynamic range of the image. Conclusion: The deep learning-based cystoscopy image enhancement method is significantly better than other traditional methods in both qualitative and quantitative evaluation. The application of artificial intelligence will provide clearer, higher contrast cystoscopy images for medical diagnosis.

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.

Automatic Detection of Cast Billet Dendrite Based on Improved Hough Transform

Primary dendrite information is one of the most important metrics to measure the quality of continuous cast slabs. The contrast of low magnification images is very low under the influence of illumination and sampling devices, so the traditional dendrite detection method has the problem of missed detections. We propose an automatic dendrite detection method based on an improved Hough transform, which effectively improves the accuracy and efficiency of primary dendrite detection. By using the local grayscale features of the image, a genetic algorithm-based local contrast enhancement algorithm is proposed. Compared with the traditional contrast enhancement algorithm, it can retain all the information of the dendrites. Combined with the image binarization method based on Hessian matrix, we can obtain more detailed information about the dendrites. According to the continuity and solidification characteristics of dendrites, the Hough transform is improved to extract dendrite information, which effectively reduces the computational cost of the Hough transform. The experimental results show that the method of this paper has versatility, and the error is four pixels compared with the manual method, which can provide a reliable basis for the subsequent judgement of the quality of cast billets.

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.

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.

Bioinspired Photoreceptors with Neural Network for Recognition and Classification of Sign Language Gesture.

This work addresses the design and implementation of a novel PhotoBiological Filter Classifier (PhBFC) to improve the accuracy of a static sign language translation system. The captured images are preprocessed by a contrast enhancement algorithm inspired by the capacity of retinal photoreceptor cells from mammals, which are responsible for capturing light and transforming it into electric signals that the brain can interpret as images. This sign translation system not only supports the effective communication between an agent and an operator but also between a community with hearing disabilities and other people. Additionally, this technology could be integrated into diverse devices and applications, further broadening its scope, and extending its benefits for the community in general. The bioinspired photoreceptor model is evaluated under different conditions. To validate the advantages of applying photoreceptors cells, 100 tests were conducted per letter to be recognized, on three different models (V1, V2, and V3), obtaining an average of 91.1% of accuracy on V3, compared to 63.4% obtained on V1, and an average of 55.5 Frames Per Second (FPS) in each letter classification iteration for V1, V2, and V3, demonstrating that the use of photoreceptor cells does not affect the processing time while also improving the accuracy. The great application potential of this system is underscored, as it can be employed, for example, in Deep Learning (DL) for pattern recognition or agent decision-making trained by reinforcement learning, etc.

Contrast Enhancement of Alzheimer’s MRI using Histogram Analysis

Contrast enhancement of MRI images frequently needs considerable pre-processing to provide accurate data for disease diagnosis and proper treatment. Enhancing the appearance of medical images becomes a difficult task owing to the uncertainty of the obtained image quality. In this study, Alzheimer’s MRI images are subjected to a contrast enhancement algorithm for easy diagnosis. A noise reduction and contrast enhancement technique for MRI images is discussed in this research. Histogram-based algorithms are used to solve the problems of de-noising and enhancing the contrast of images for identification of the infected region. The proposed method is based on contrast-limited adaptive histogram equalization (CLAHE) and the comparison with Histogram Equalization (HE). The suggested enhancement technique's performance can be evaluated using several metrics, including Structure Similarity Index Measure (SSIM), and Peak Signal-to-Noise Ratio (PSNR). Observational studies revealed that the suggested approach is significantly more efficient than the basic enhancement techniques such as HE.

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.

A Low-Delay Dynamic Range Compression and Contrast Enhancement Algorithm Based on an Uncooled Infrared Sensor with Local Optimal Contrast

Real-time compression of images with a high dynamic range into those with a low dynamic range while preserving the maximum amount of detail is still a critical technology in infrared image processing. We propose a dynamic range compression and enhancement algorithm for infrared images with local optimal contrast (DRCE-LOC). The algorithm has four steps. The first involves blocking the original image to determine the optimal stretching coefficient by using the information of the local block. In the second, the algorithm combines the original image with a low-pass filter to create the background and detailed layers, compressing the background layer with a dynamic range of adaptive gain, and enhancing the detailed layer for the visual characteristics of the human eye. Third, the original image was used as input, the compressed background layer was used as a brightness-guided image, and the local optimal stretching coefficient was used for dynamic range compression. Fourth, an 8-bit image was created (from typical 14-bit input) by merging the enhanced details and the compressed background. Implemented on FPGA, it used 2.2554 Mb of Block RAM, five dividers, and a root calculator with a total image delay of 0.018 s. The study analyzed mainstream algorithms in various scenarios (rich scenes, small targets, and indoor scenes), confirming the proposed algorithm’s superiority in real-time processing, resource utilization, preservation of the image’s details, and visual effects.

A new approach of contrast enhancement for Medical Images based on entropy curve

X-ray, MRI, and CT scan images etc, are widely used for the detection of various prevalent diseases. The efficient identification of the disease depends very much on the quality of the image. Sometimes due to environmental conditions such as insufficient and nonuniform illumination, shacking and movement of objects or the imaging device, and some other fault in the device, the resultant image may have low contrast and the presence of noise or artifacts. In such situation, a contrast enhancement algorithm that can efficiently improve the contrast without increasing noise and artifacts can play an important role. In this article, we propose a simple yet efficient method for these types of images by using the image’s entropy curve and homomorphic filtering. It enhances the contrast of medical images without increasing the content of noise and artifacts. Also, the performance of the proposed method is evaluated by a complete qualitative and quantitative analysis.

AC-Faster R-CNN: an improved detection architecture with high precision and sensitivity for abnormality in spine x-ray images.

Objective.In clinical medicine, localization and identification of disease on spinal radiographs are difficult and require a high level of expertise in the radiological discipline and extensive clinical experience. The model based on deep learning acquires certain disease recognition abilities through continuous training, thereby assisting clinical physicians in disease diagnosis. This study aims to develop an object detection network that accurately locates and classifies the abnormal parts in spinal x-ray photographs.Approach.This study proposes a deep learning-based automated multi-disease detection architecture called Abnormality Capture-Faster Region-based Convolutional Neural Network (AC-Faster R-CNN), which develops the feature fusion structure Deformable Convolution Feature Pyramid Network and the abnormality capture structure Abnormality Capture Head. Through the combination of dilated and deformable convolutions, the model better captures the multi-scale information of lesions. To further improve the detection performance, the contrast enhancement algorithm Contrast Limited Adaptive Histogram Equalization is used for image preprocessing.Main results.The proposed model is extensively evaluated on a testing set containing 1007 spine x-ray images and the experimental results show that the AC-Faster R-CNN architecture outperforms the baseline model and other advanced detection architectures. The mean Average Precision at Intersection over Union of 50% are 39.8%, the Precision and Sensitivity at the optimal cutoff point of Precision-Recall curve are 48.6% and 46.3%, respectively, reaching the current state-of-the-art detection level.Significance.AC-Faster R-CNN exhibits high precision and sensitivity in abnormality detection tasks of spinal x-ray images, and effectively locates and identifies abnormal areas. Additionally, this study would provide reference and comparison for the further development of medical automatic detection.

Modified chameleon swarm algorithm for brightness and contrast enhancement of satellite images

Modified chameleon swarm algorithm for brightness and contrast enhancement of satellite images

Enhanced Infrared Detection Algorithm for Weak Targets in Complex Backgrounds

In this article, we design a new lightweight infrared optical system that fully meets airborne settings and greatly reduces the collection of invalid information. This new system targets the technical problems of stray light, strong invalid information, weak texture information of small targets, and low intensity of valid information under a complex background, which lead to difficult identification of small targets. Image enhancement of weak, small targets against complex backgrounds has been the key to improving small-target search and tracking technology. For the complex information that is still collected, an improved two-channel image enhancement processing algorithm is proposed: the A-channel adopts an improved nonlinear diffusion method and improved curvature filtering, and the B-channel adopts bootstrap filtering and a local contrast enhancement algorithm. The weak target is then extracted by the algorithm of weighted superposition. The false alarm rate is effectively weakened, and robustness is improved. As a result of the experimental data analysis, the method can effectively extract the weak targets in complex backgrounds, such as artificial backgrounds, surface vegetation, etc., enlarge the target gray value, and reduce Fa by 56%, compared with other advanced methods, while increasing Pd by 17%. The algorithm proposed in this paper is of great significance and value for weak target identification and tracking, and it has been successfully applied to industrial detection, medical detection, and in the military field.

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.

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.

SLAAHE: Selective Apex Adaptive Histogram Equalization

Medical imaging enables specialists to make diagnoses our internal body organs for detecting anomalies without being intrusive. Unfortunately, the images produced by various modalities are not only plagued by noises, but the images contrasts are also subpar, making it impossible to use them for a delicate task like diagnosis, which is directly concerned with our health. Pre-processing the image becomes obligatory to ameliorate its medical worth and contrast enhancement is a sacrosanct part of diagnostic image pre-processing. Even though some popular contrast enhancement algorithms, such as Adaptive Histogram Equalization (AHE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and Minimum Mean Brightness Error Bi-Histogram Equalization (MMBEBHE), etc., are widely used, they are insufficient for some medical imaging modalities, such as ultrasound imaging (USG). This paper, named as Selective Apex Adaptive Histogram Equalization (SLAAHE), proposes an effective and efficient contrast enhancement algorithm that dynamically selects the best window size for dividing the low contrast image into image grids and clip limit for clipping the histogram of the low contrast sub-image only. Unique feature of the proposed algorithm is that it clips the image histogram only when it is not well-distributed and ennobles the output image contrast. This makes the algorithm best concerning the contrast enhancement quality, and reduced time and space complexity. Both theoretical analysis and assessment of experimental results with statistical metrics like Universal Image Quality Index (UIQI), Absolute Mean Brightness Error (AMBE), Multi-Scale Structural Similarity Index Measure (MSSSIM), Entropy, and Root Mean Square Error (RMSE) exhibit how the proposed method has gained momentous improvement over existing state-of-the-art methods. For the evaluation metrics UIQI, AMBE, MSSSIM, Entropy, and RMSE, the suggested technique received average scores of 0.79, 0.12, 0.88, 0.92, and 11.52 respectively.

Role of instrumental factors in Meibomian gland contrast assessment.

Meibomian gland contrast has been suggested as a potential biomarker in Meibomian gland dysfunction. This study analysed the instrumental factors related to contrast. The objectives were to determine whether the mathematical equations used to compute gland contrast (e.g., Michelson or Yeh and Lin), impact the ability to identify abnormal individuals, to ascertain whether contrast between the gland and the background could be an effective biomarker and to assess whether using contrast-enhancement on the gland image improves its diagnostic efficacy. A total of 240 meibography images from 40 participants (20 controls and 20 having Meibomian gland dysfunction or blepharitis), were included. The Oculus Keratograph 5M was used to capture images from the upper and lower eyelids of each eye. The contrast of unprocessed images and those pre-processed with contrast-enhancement algorithms were analysed. Contrast was measured on the eight central glands. Two equations for contrast computation were used, and the contrast both between glands and within a gland were calculated. Significant differences were found between the groups for inter-gland area in the upper (p = 0.01) and lower eyelids (p = 0.001) for contrast measured with the Michelson formula. Similar effects were observed when using the Yeh and Lin method in the upper (p = 0.01) and lower eyelids (p = 0.04). These results were obtained for images enhanced with the Keratograph 5M algorithm. Meibomian gland contrast is a useful biomarker of disease related to the Meibomian glands. Contrast measurement should be determined using contrast-enhanced images in the inter-gland area. However, the method used to compute contrast did not influence the results.