Adaptive Image Enhancement Research Articles

Dynamic two-phase flow behaviors in permeable network integrated with microchannel

The performance of a direct methanol fuel cell greatly depends on the two-phase flow behaviors in the anode gas diffusion layer and flow channel. However, complex and opaque nature of gas diffusion layer poses a grand challenge for deep insight into the two-phase flow behaviors. Therefore, a homogeneous permeable network integrated with a microchannel was put forward to simulate the anode gas diffusion layer and microchannel in the present study, by which the dynamic two-phase flow behaviors were visually studied with the help of adaptive image enhancement method. The influence of the position and number of the gas inlet, and the gas and liquid flow rates on the two-phase flow behavior were analyzed. The results showed that the liquid saturation decreased and the number of the position for bubble generation increased with increasing the gas flow rate in case the gas was suppled from only one gas supply orifice. When two gas supply orifices simultaneously supplied the gas, the liquid saturation was between the cases using a single one gas supply orifice. For given liquid and gas flow rates of 0.1 mL/min and 0.5 mL/min, the liquid saturation was about 84%, which was between 83% and 92% for the single-one cases. Besides, the two-phase flow exhibited strong periodicity at low gas flow rates. The obtained results provide a deep understanding of the two-phase flow behaviors in such a simulated structure, helping better design and operation of direct methanol fuel cells.

Detection of Logos of Moving Vehicles under Complex Lighting Conditions

This study proposes a method for vehicle logo detection and recognition to detect missing and inaccurate vehicle marks under complex lighting conditions. For images acquired in complex light conditions, adaptive image enhancement is used to improve the accuracy of car sign detection by more than 2%; for the problems of multi-scale and detection speed of vehicle logo recognition in different images, the paper improves the target detection algorithm to improve the detection accuracy by more than 3%. The adaptive image enhancement algorithm and improved You Only Look One-level Feature (YOLOF) detection algorithm proposed in this study can effectively improve the correct identification rate under complex lighting conditions.

Adaptive image enhancement and dynamic-template-matching-based edge extraction method for diamond roller on-machine profile measurement

Adaptive image enhancement and dynamic-template-matching-based edge extraction method for diamond roller on-machine profile measurement

Adaptive Fractional Image Enhancement Algorithm Based on Rough Set and Particle Swarm Optimization

This paper proposes a new image enhancement algorithm. At first, the paper uses the combination of rough set and particle swarm optimization (PSO) algorithm to distinguish the smooth area, edge and texture area of the image. Then, according to the results of image segmentation, an adaptive fractional differential filter is used to enhance the image. Finally, the experimental results show that the image enhanced by this algorithm has clear edge, rich texture details, and retains the information of the smooth area of the image.

Spatially adaptive multi-scale image enhancement based on nonsubsampled contourlet transform

Low or uneven luminance results in low contrast of near-infrared and optical remote sensing images, making it challenging to analyze their contents. Traditional image enhancement methods cannot simultaneously take detail preservation, contrast enhancement, and brightness improvement into account. In order to cope with this problem, this paper proposes a spatially adaptive multi-scale image enhancement (SAMSIE) scheme, including three key procedures: First, nonsubsampled contourlet transform (NSCT) is employed to decompose a low-contrast image into multi-scale layers. Second, a spatially adaptive Gamma correction strategy based on improved histogram equalization is proposed to enhance the base layer which is used as a guide layer. Third, an adaptive enhancement operator is proposed to enhance fine details. Finally, a high-contrast optical infrared image is obtained by the inverse NSCT with usage of these enhanced layers. The effectiveness of the proposed SAMSIE method is validated by both visualization assess and the evaluation of three quantitative indexes including discrete entropy (DE), contrast gain (CG), and mean brightness improvement (MBI), with comparison of the state-of-the-arts.

Retracted] Film and Television Animation Sensing and Visual Image by Computer Digital Image Technology

In order to study the application of computer digital image processing technology in film and television (FAT) animation visual sensing expression, by studying the principle of digital image processing technology and visual sensing technology, a spatial image adaptive steganography image enhancement algorithm by multiscale filters is proposed to carry out enhancement processing of the original image in FAT production. This algorithm can provide more high‐quality and refined original materials for FAT animation production, which is convenient for FAT animation postproduction to produce higher‐resolution and clear FAT works. Finally, the algorithm is verified. The results show that the spatial image adaptive steganography image enhancement algorithm has high security, and the highest average detection error rate is 25.06%. When α = 0.4, the security of the spatial image adaptive steganography image enhancement algorithm is up to 34.62% and the image distortion rate is low. The established image enhancement algorithm can significantly improve the security of the existing spatial image steganography algorithm under different embedding rates, especially at a high embedding rate; the improvement of the spatial domain steganography algorithm is greater. The proposed steganographic image enhancement algorithm by image preprocessing has higher security and better image enhancement effect.

An Adaptive Low-illumination Image Enhancement Algorithm based on Weighted Least Squares Optimization

An adaptive low-illumination image enhancement algorithm based on the weighted least squares optimization is proposed to solve the difficulty of detailed feature recognition in low-illumination images that collected by visible light imaging equipment. First, the image is converted from RGB channel to LAB channel. Second, we use an edge-preserving smoothing operator based on the weighted least squares optimization to coarsen smooth base layer and extract multi-scale details in brightness channel. Then, an adaptive weight is proposed and applied to the weighted fusion of smooth base and detail features. Finally, the Retinex enhancement is performed to obtain a ultimate enhanced image. Experiments result show that the image enhanced by this method has suitable visual brightness and clear details. In terms of objective indicators, it has good and stable performance in NIQE, TMQI, and information entropy.

Blind Adaptive Structure-Preserving Imaging Enhancement for Low-Light Condition

In this letter, a novel and effective algorithm based on Retinex model is proposed for low-light image enhancement, named Blind Adaptive Structure-Preserving Image Enhancement (BASSY). The low-light image enhancement is still a challenging task because the decomposition of images into light components and reflection components is an ill-posed problem. BASSY adopts a content-adaptive guided filtering based on local variances to estimate the proper illumination map. The salient features of the proposed approach are: (1) For the illumination component, the overall structure in the low-light image is preserved and the texture details are smoothed. (2) The reflectance is estimated without logarithmic transformation to reduce the computational burden and to avoid over-smoothing the reflectance component. (3) The adaptive gamma correction for the illumination map is used to reconstruct the enhanced image. (4) BASSY can be implemented efficiently due to the low computation complexity Ο(N). Experimental results on six public datasets show that the enhanced images by the BASSY exhibit higher naturalness and better visual quality than six state-of-the-art methods.

Comparison of Image Enhancement Algorithms for Improving the Visual Quality in Computer Vision Application

Computer vision has its numerous real-world applications on Visual Object Tracking which includes human-computer interaction, autonomous vehicles, robotics, motion-based recognition, video indexing, surveillance and security, human-computer interaction, autonomous vehicles, robotics, motion-based recognition, video indexing, surveillance and security. The factors affecting the tracking process is due to low illumination, haze and cloudy environment and noisy environment. In this paper, we aim to extensively review the latest trends and advances in adaptive enhancement algorithm and evaluate the performance using Full reference like, SSIM (Structure Similarity Index Measure), MS-SSIM (Multi-scale Structure Similarity Index Measure), ESSIM (Edge Strength Structural Similarity Index), FSIM (Feature Similarity Index Measure), VIF (Visual Information Fidelity), CW-SSIM (complex wavelet structural similarity), UQI (Universal Quality Index), IEF (Image Enhancement Factor), IQI (Image Quality Index), EME (Enhancement Measurement Error), CVSI (Contrast and Visual Salient Information), MCSD (Multiscale contrast similarity deviation), NQM (Noise Quality Measure), Gradient Magnitude Similarity Mean (GMSM), Gradient Magnitude Similarity Deviation (GMSM) and no-reference image quality measures Perception based Image Quality Evaluator (PIQE), Blind/Reference less Image Spatial Quality Evaluator (BRISQUE), Naturalness Image Quality Evaluator (NIQE), Average Gradient (AG), Contrast, Information Entropy (IE), Lightness order Error (LOE). The main purpose of adaptive image enhancement is to smooth the uniform area and sharpen the border of an image to improve its visual quality. In this paper, fourteen image enhancement algorithms were tested on LoL dataset to benchmark the time taken to process them and their output quality was evaluated. Results from this study will give insights to image analysts for selecting image enhancement algorithms which acts as a pre- processing stage for Visual object Tracking.

Nonlocally adaptive image enhancement system for full-field optical coherence tomography

Full-field optical coherence tomography (FFOCT) can be used to acquire cellular-level morphological images of nonfixed and unstained biological tissues. However, the speckle noise and vignetting of FFOCT images are drawbacks in terms of pathological application. We present a novel adaptive nonlocal image enhancement algorithm that uses a fluorescence microscopy (FM) image of the nucleus to achieve cytoplasm image enhancement under FFOCT. The algorithm comprises a denoising module and an adaptive nonlocal enhancement module. The denoising module is an end-to-end residual network with skip connections and dilated convolution. The adaptive nonlocal enhancement module enhances images to segment tissues, nontissues, and transition areas so that the images retain the details of the original cell structure but do not contain noise. The proposed algorithm was implemented on a dual-modality scanner system that combines FFOCT and FM. A total of 12 pig liver and pig kidney tissues were sectioned into 10,236 samples for experiments. The peak signal-to-noise ratio and structural similarity index value increased from 30.8 and 0.58, respectively, for the aforementioned system without the proposed algorithm to 34.2 and 0.86, respectively, for the aforementioned system with the proposed algorithm. Ten samples of human breast tissues were used to validate the proposed algorithm. The average contrast-to-noise ratio was 1.69 and 11.73 when the aforementioned system was used with and without the proposed algorithm, respectively. The results of this study revealed that the quality of scanned images considerably improved when the proposed system was used and that the proposed system can facilitate clinical pathological diagnosis.

Genetic Algorithm Based Fuzzy Local Informationc-Means (Gaflicm) Clustering Algorithm And Hybrid Kernel Convolution Neural Network (Hkcnn) With Distributed Processing Framework For Brain Mri Images

Cancer is the large important origin of casualty in today world. Among many cancers, brain cancer has been becomes one of the lowest living rate. It is formed based on the brain tumor. However brain tumors are able to have diverse categories based on their shape, texture, and position. Appropriate identification and retrieval of the tumor types create potential the doctor to compose the right cure option and assist keep the patient's life. Image processing has gained wide attention in medical analysis and health in recently. In general image processing methods, brain Magnetic Resonance Imaging (MRI) image collections cannot be processed efficiently on one computer due to large collection sizes and high computational costs. Hence, parallel computing and distributed system has been performed increasingly for brain MRI images in recently. In this paper, a novel Medical Image Cloud Processing (MICP) based distributed processing framework is proposed for brain MRI images by lesser computational time. In this work, image preprocessing is done by using the Adaptive Median Filtering (AMF) and image enhancement by Histogram Equalization (HE). The proposed MICP framework includes of Static Medical Image Cloud Processing (SMICP) and Dynamic Medical Image Cloud Processing (DMICP). In MICP framework, SMICP consists of two methods called Pure-Image and Big-Image. These methods are integrated to Genetic Algorithm based Fuzzy Local Information C-Means-MapReduce (GAFLICM-MR) algorithm to attain more optimized design and higher effectiveness. The core design of GAFLICM-MR framework is to make use of the rich computing resources given by means of the distributed system consequently as to apply efficient parallel processing. GAFLICM algorithm is also used as brain tumor segmentation in MRI images. In MICP framework, DMICP is developed via a parallel processing process of the distributed system. For retrieval and detection of brain Magnetic MRIimages into normal and tumor, Hybrid Kernel Convolution Neural Network (HKCNN) is developed in this work. Finally the results of the HKCNN classifier are compared to other previous works like precision, recall, f-measure, accuracy, time and memory.

Genetic algorithm based adaptive histogram equalization (GAAHE) technique for medical image enhancement

In Magnetic Resonance Imaging (MRI), the poor quality images may not provide the sufficient information for the visual interpretation of the affected locations of human body. So, to improve the image visions and to provide computational support, a novel adaptive image enhancement technique has been proposed in this paper, named as genetic algorithm based adaptive histogram equalization (GAAHE) technique. The proposed framework includes genetic algorithm, histogram sub-division and modified probability density function (PDF). A novel approach of subdivision is applied to the histogram using the exposure threshold and optimal threshold for preserving the brightness and reducing the information loss. To make the proposed technique more adaptive, the threshold parameters are optimized by utilizing the concept of genetic algorithm, guided by the proposed multi-objective fitness function. Then, the PDF of each sub-histogram is modified to enhance the image quality. The experimental results show that, the proposed GAAHE technique performs superior over other existing enhancement techniques.

An improved infrared image adaptive enhancement method

An improved infrared image adaptive enhancement method

SE–RWNN: an synergistic evolution and randomly wired neural network‐based model for adaptive underwater image enhancement

Under water images are likely to suffer from severe degradation such as colour distortion, low contrast, and fuzz content, caused by the absorption and scattering effects of the water. To improve the visual appearance of the image, the authors present an adaptive algorithm for effective underwater image enhancement using a randomly wired neural network (RWNN) and synergistic evolution (SE). In doing so, they sequentially conduct colours adjustment, contrast improvement and luminance enhancement while enhancing details by an edge-preserving technique. To set up the system, they develop a multi-strategy cooperating evolution algorithm to figure out the optimal parameter values. Extensive experimental results show that the proposed model improves both subjectively and quantitatively the quality of underwater images.

Learning an adaptive model for extreme low‐light raw image processing

Low-light images suffer from severe noise and low illumination. In this work, the authors propose an adaptive low-light raw image enhancement network to avoid parameter-handcrafting in current deep learning models and to improve image quality. The proposed method can be divided into two sub-models: brightness prediction and exposure shifting (ES). The former is designed to control the brightness of the resulting image by estimating a guideline exposure time . The latter learns to approximate an exposure-shifting operator ES, converting a low-light image with real exposure time to a noise-free image with guideline exposure time . Additionally, structural similarity loss and image enhancement vector are introduced to promote image quality, and a new campus image data set (CID) for training the proposed model is proposed to overcome the limitations of the existing data sets. In quantitative tests, it is shown that the proposed method has the lowest noise level estimation score compared with the state-of-the-art low-light algorithms, suggesting a superior denoising performance. Furthermore, those tests illustrate that the proposed method is able to adaptively control the global image brightness according to the content of the image scene. Lastly, the potential application in video processing is briefly discussed.

Research on Image Adaptive Enhancement Algorithm under Low Light in License Plate Recognition System

The traffic block port monitors and manages the road traffic by shooting and recording the motor vehicles. However, due to the complex factors such as shooting angle, light condition, environmental background, etc., the recognition rate of license plate is not high enough. High light and low light under complex lighting conditions are symmetry problems. This paper analyzes and solves the low light problem in detail, an image adaptive enhancement algorithm under low light conditions is proposed in the paper. The algorithm mainly includes four modules, among which, the fast image classification module uses the deep and separable convolutional neural network to classify low-light images into low-light images by day and low-light images by night, greatly reducing the computation burden on the basis of ensuring the classification accuracy. The image enhancement module inputs the classified images into two different image enhancement algorithms and adopts the idea of dividing and ruling; the image quality evaluation module adopts a weighted comprehensive evaluation index. The final experiment shows that the comprehensive evaluation indexes are all greater than 0.83, which can improve the subsequent recognition of vehicle face and license plate.

Infrared Image Adaptive Enhancement Guided by Energy of Gradient Transformation and Multiscale Image Fusion

The detail enhancement and dynamic range compression of infrared (IR) images is an important issue and a necessary practical application in the domain of IR image processing. This paper provides a novel approach to displaying high dynamic range infrared images on common display equipment with appropriate contrast and clear detail information. The steps are chiefly as follows. First, in order to protect the weak global details in different regions of the image, we adjust the original normalized image into multiple brightness levels by adaptive Gamma transformation. Second, each brightness image is decomposed into a base layer and several detail layers by the multiscale guided filter. Details in each image are enhanced separately. Third, to obtain the image with global details of the input image, enhanced images in each brightness are fused together. Last, we filter out the outliers and adjust the dynamic range before outputting the image. Compared with other conventional or cutting-edge methods, the experimental results demonstrate that the proposed approach is effective and robust in dynamic range compression and detail information enhancement of IR image.

Adaptive Multi-Scale Image Enhancement for Digital Radiography.

Digital radiography has been increasingly adopted since it can provide better image quality compared to conventional screen/film method. However, digital radiography can sometimes produce low-quality images because its processing algorithm is unaware of the content. Here, an adaptive multi-scale image enhancement algorithm for digital radiography is demonstrated. The algorithm adapts to the context of the image, thus providing better image quality. The qualitative and quantitative validations of the algorithm in phantoms and in clinical settings showed satisfactory performance.

Adaptive image enhancement method using contrast limitation based on multiple layers BOHE

Multiple layers block overlapped histogram equalization (MLBOHE) is a classic image enhancement method. However, median filter is used in it to reduce noises which cause the degeneration of the local information. Moreover, the hidden details are not revealed effectively during the image fusion processes. To solve these drawbacks, an adaptive image enhancement method using contrast limitation is proposed in this paper. Based on MLBOHE, the proposed method employs a contrast limited method to suppress noises before BOHE is performed in each layer sub-blocks. Then an improved image fusion mode is applied to adaptively merge the multilayered BOHE images. The way to obtain the fusion weights by this fusion mode is according to the entropy value of each layer sub-blocks. In addition, four Image Quality Measures (IQMs), namely peak signal-to-noise ratio (PSNR), image clarity, contrast measure (EME) and feature similarity index metric (FSIM), are used to analyze the effectiveness of the proposed method. Simulation results show that the proposed method has high performance in suppressing noises and displaying more trustworthy details. Besides, this method outperforms the existing methods in weakening the excessive enhancement for low illumination and foggy images.

Research on defect inspection method of pipeline robot based on adaptive image enhancement

In view of the problem about uneven image acquisition and inaccurate edge extraction in pipeline detection process, a pipeline robot defect inspection method based on adaptive image enhancement is proposed. Firstly, a single-scale Retinex adaptive image enhancement algorithm is designed, which uses the guided filter to estimate the illumination component of the Value component of the image, and gets the illumination equilibrium image by adaptive Gamma correction, so as to realize the image enhancement. Then, the traditional Canny edge detection method is improved, using bilateral filtering to smooth the image. Besides, the defect images are segmented by the iterative threshold method, and the edge connection is carried out according to the edge pixel similarity. Therefore, the defect contour of the pipe-wall is extracted effectively. Thirdly, a pipeline robot defect detection system based on adaptive image enhancement is built, and a crawler car equipped with the pan-tilt-zoom camera conducts all-round visual inspection of the defects in the pipeline inner wall. The experimental results show that the detection method in this paper can adaptively correct the image brightness, and the uneven brightness of the image is significantly improved. Compared with the sub-optimal algorithm, the information entropy of the image is increased by 2.4%, the average gradient of the image is increased by 2.3%, and the peak signal to noise ratio is increased by 4.4%, and the pipeline defect edges are extracted effectively with the detection accuracy up to 97%.