Image Enhancement Applications Research Articles

Light image enhancement and virtual reality application in automatic generation of basketball game scenes and training data simulation

Light image enhancement and virtual reality application in automatic generation of basketball game scenes and training data simulation

Engineering Atomic-Scale Patterning and Resistive Switching in 2D Crystals and Application in Image Processing.

The ultrathin thickness of 2D layered materials affords the control of their properties through defects, surface modification, and electrostatic fields more efficiently compared with bulk architecture. In particular, patterning design, such as moiré superlattice patterns and spatially periodic dielectric structures, are demonstrated to possess the ability to precisely control the local atomic and electronic environment at large scale, thus providing extra degrees of freedom to realize tailored material properties and device functionality. Here,the scalable atomic-scale patterning in superionic cuprous telluride by using the bonding difference at nonequivalent copper sites is reported. Moreover, benefitting from the natural coupling of ordered and disordered sublattices,controllable piezoelectricity-like multilevel switching and bipolar switching with the designed crystal structure and electrical contact is realized, and their application in image enhancement is demonstrated. This work extends the known classes of patternable crystals and atomic switching devices, and ushers in a frontier for image processing with memristors.

Learning by competing: Competitive multi-generator based adversarial learning

Generative adversarial networks (GANs) have been extensively used for dozens of image enhancement and image translation applications, where several traditional and novel architectures have been and are still being introduced. However, the classical training protocols do not fully explore the significant potential of such architectures. In this paper, we propose a novel conditional GAN (cGAN) framework called competitive multi-generator based cGAN (CMcGAN) for more effective training progress and enhanced image generation. Different from classical adversarial learning protocols, the generators of a CMcGAN are not only challenged by the discriminator for improved performance, but also by other generators. This is achieved by a sharing mechanism, where the current performances of the competing generators are shared among each other, motivating each generator to perform better than the rest. Extensive experiments in several conditional image generation applications show the significant improvements achieved by several traditional architectures when trained following our proposed learning strategy. Moreover, these architectures, trained by our learning strategy, outperformed the state-of-the-art approaches proposed in each tested application.

QRFODD: Quaternion Riesz fractional order directional derivative for color image edge detection

Edge detection is the prominent method to determine the discontinuities present in an image. There exists an issue of loss of information and correlation during the extraction of edges in color images. To resolve it, a novel quaternion domain based Riesz fractional order directional derivative approach is presented in this paper to extract more edge details in color images. The Riesz fractional masks obtained by utilizing Aitken interpolation are applied on color images in the quaternion domain for yielding the edge map as there is no need for recalculation of basis polynomials in case of the addition of any data point. The performance of the proposed quaternion based technique is analyzed on the benchmark BSDS300 and BSDS500 datasets in the terms of Figure of Merit and F-Score. The results obtained exhibit the efficiency of proposed technique over the classical and state-of-the-art edge detection approaches. The robustness of this approach is further established by taking into account uncontrolled conditions such as noise, JPEG compression, and varying illumination as it provided more features than the classical integer-order detectors. Moreover, the superiority of the proposed quaternion based technique is validated for medical images as well as its application in image enhancement.

Novel image processing method inspired by wavelet transform.

I developed a novel image processing method inspired by wavelet transform that uses a concentric mother wavelet. This method can be used for noise filtering, feature extraction, image enhancement, and other applications and is effective for images with low spatial frequencies. When filtering small-sized images, setting masks for Fourier filtering is often difficult; however, the proposed method does not require mask setting, thus facilitating filtering. In practice, many concentric mother wavelets of different frequencies are prepared in advance, and the cross-correlation between the input image and prepared wavelets is calculated. Reconstruction or image processing can be easily performed by combining the obtained results for each frequency. I believe that the present method can aid image processing related to material analysis.

Image Enhancement of Low Light Image using Deep Learning

Image enhancement of low light images is an important research area in computer vision and image processing. In recent years, deep learning has emerged as a powerful tool for enhancing low light images, with convolutional neural networks (CNNs) being the most used architecture. In this study, we propose a deep learning approach for enhancing low light images using a modified CNN. Our approach involves training the network on a large dataset of low light images and their corresponding enhanced images and using the trained model to enhance new low light images. We also propose a new loss function that encourages the network to enhance details and reduce noise in the images. Our experiments show that our approach outperforms existing state-of-the-art methods in terms of both objective metrics and visual quality, making it a promising technique for real-world low light image enhancement applications

Underwater self-supervised monocular depth estimation and its application in image enhancement

Underwater self-supervised monocular depth estimation and its application in image enhancement

The Influence of X-Ray Tube Current-Time Variations Toward Signal to Noise Ratio (SNR) in Digital Radiography: A Phantom Study

Several image enhancement applications can be used to increase SNR and brightness. However, if the setting of tube current-time is too high, the application can not reduce the radiation dose. So, It is necessary to test tube current-time variations on the SNR value and image quality to determine the effect on body organs and radiation dose. This study aims to determine the influence of tube current-time on SNR and with this research is expected to be an input for radiographers in the field to pay attention to parameter settings for patients. We compared five tube current-time variations on the Antebrachii examination by using Phantom (58 kV, 32 mA, 50 mSec), (58 kV, 100 mA, 50 mSec), (58 kV, 32 mA, 5 mAs), (58 kV , 32 mA, 5 mAs) and (58kV, 100 mA, 5 mAs). Then we made a circle of ROI with a diameter of 0.2 cm2 on the Bone, Soft-tissue and Air sections in that four tube current-time variations, each of which has 7 ROI points tocalculate the SNR and the radiation dose is seen from the image. There is a difference in SNR on tube current-time changes and affects the image quality with an Adjusted R square value of 0.003 and 0.865. It shows that there is a change in tube current that affects the Signal to Noise Ratio between organs. Since it is proven that changes in tube current-time affect image quality, further research can formulate an ideal parameter for the examination of each organ.

Generalised homomorphic and root filtering in 2D-nonseparable discrete linear canonical transform domains in the image enhancement applications

Generalised homomorphic and root filtering in 2D-nonseparable discrete linear canonical transform domains in the image enhancement applications

Low area FPGA implementation of modified histogram estimation architecture with CSAC-DPROM-OBC for medical image enhancement application

Low area FPGA implementation of modified histogram estimation architecture with CSAC-DPROM-OBC for medical image enhancement application

A deep journey into image enhancement: A survey of current and emerging trends

Image captured under poor-illumination conditions often display attributes of having poor contrasts, low brightness, a narrow gray range, colour distortions and considerable interference, which seriously affect the qualitative visual effects on human eyes and severely restrict the efficiency of several machine vision systems. In addition, underwater images often suffer from colour shift and contrast degradation because of an absorption and scattering of light while travelling in water. These unpleasant effects limits visibility, reduce contrast and even generate colour casts that limits the use of underwater images and videos in marine archaeology and biology. In medical imaging applications, medical images are important tools for detecting and diagnosing several medical conditions and ailments. However, the quality of medical images can often be degraded during image acquisition due to factors such as noise interference, artefacts, and poor illumination. This may lead to the misdiagnosis of medical conditions, which can further aggravate life threatening situations. Image enhancement is one of the most important technologies in the field of image processing, and its purpose is to improve the quality of images for specific applications. In general, the basic principle of image enhancement is to improve the quality and visual interpretability of an image so that it is more suitable for the specific applications and the observers. Over the last few decades, numerous image enhancement techniques have been proposed in the literature This study covers a systematic survey on existing state-of-the-art image enhancement techniques into broad classification of their algorithms. In addition, this paper summarises the datasets utilised in the literature for performing the experiments. Furthermore, an attention has been drawn towards several evaluation parameters for quantitative evaluation and compared different state-of-the-art algorithms for performance analysis on benchmark datasets. In addition, we discussed the recent areas of applications in image enhancement in detail. Lastly, we have also discussed numerous unresolved open problems and suggested possible future research directions. We believe that by putting forth all our efforts this study may presents a comprehensive resource for the future research.

COVID-19 detection based on pre-trained deep networks and LSTM model using X-ray images enhanced contrast with artificial bee colony algorithm.

Coronavirus (COVID-19) is an infectious disease that has spread across the world within a short period of time and is causing rapid casualties. The main symptoms of this virus are shortness of breath, fever, cough, and a sore throat. The virus is detected through samples, such as throat swabs and sputum, taken from people who meet the possible case definition and the results are usually obtained within a few hours or a day. The development of test kits to detect the COVID-19 virus is still an open research topic, and automated and faster diagnostic tools are needed. Recent studies have shown that biomedical images can be used for COVID-19 testing. This study proposes the hybrid use of pre-trained deep networks and the long short-term memory (LSTM) for the classification of COVID-19 from contrast-enhanced chest X-rays. In the proposed system, a transformation function is applied to X-ray images first. Then, the artificial bee colony (ABC) algorithm is used to optimize the parameters obtained from the transformation function. The pre-trained deep network models and LSTM are preferred to extract features from the contrast-enhanced chest X-rays. At the final stage, COVID-19, normal (healthy), and pneumonia chest X-ray are classified using softmax. To evaluate the performance of the proposed method, the "COVID-19 radiography" dataset, which is widely used in the literature, is preferred. From the proposed model, 98.97% accuracy, 98.80% precision, and 98.70% sensitivity rates are obtained. Experimental results reveal that the proposed model provides efficient results compared to other methods. Thanks to the application of ABC-based image enhancement, increased classification of 2.5% has been achieved against other state-of-the-art models.

Single image defocus map estimation through patch blurriness classification and its applications

Depth information is useful in many image processing applications. However, since taking a picture is a process of projection of a 3D scene onto a 2D imaging sensor, the depth information is embedded in the image. Extracting the depth information from the image is a challenging task. A guiding principle is that the level of blurriness due to defocus is related to the distance between the object and the focal plane. Based on this principle and the widely used assumption that Gaussian blur is a good model for defocus blur, we formulate the problem of estimating the spatially varying defocus blurriness as a Gaussian blur classification problem. We solve the problem by training a deep neural network to classify image patches into one of the 20 levels of blurriness. We have created a dataset of more than 500,000 image patches of size 32times 32 which does not require human labelling. The dataset is used to train and test several well-known network models. We find that MobileNetV2 is suitable for this application due to its low memory requirement and high accuracy. The trained model is used to determine the patch blurriness which is then refined by applying an iterative weighted guided filter. The result is a defocus map that carries the information of the degree of blurriness for each pixel. We compare the proposed method with state-of-the-art techniques and we demonstrate its successful applications in adaptive image enhancement and defocus magnification limited to images that present a clear distinction between defocus levels.

Interactive and Fast Low-Light Image Enhancement Algo-rithm and Application

Abstract To obtain personalized outcomes for the low-light image enhancement, a novel interactive algorithm based on the well-designed Gamma Curve is proposed to enrich the enhancement techniques. Different from the previous works trying to enhance the image in solely brightness or naturalness by a specific designed deep network, the proposed method is capable of controlling the output results according to the user’s preferences by the same framework with different parameters. There would be three main advantages brought by the proposed network: 1) Interactivity, which allows to generate enhancements results according to users’ preferences in human-interactive manners; 2) Convenience, wherein the model only needs to train for once without using any reference images, and then can obtain results with different brightness during testing by adjusting the hyper-parameter. 3) Fastness, which results from the lightweight network and the excellent properties of the Gamma Curve to make the network operate in extraordinary high speed. Experiments demonstrate the superiority of our algorithm relative to the previous work. In addition, a multi-platform low-illumination enhancement software is explored to facilitate its application for the public.

Generalised homomorphic and root filtering in 2D-nonseparable discrete linear canonical transform domains in the image enhancement applications

Generalised homomorphic and root filtering in 2D-nonseparable discrete linear canonical transform domains in the image enhancement applications

Application of Local Histogram Clipping Equalization Image Enhancement in Bearing Fault Diagnosis

Aiming at the problem that the time-frequency image of bearing fault characteristics is relatively weak and difficult to identify. This paper presents a time-frequency analysis method of local maximum synchrosqueezing transform based on image enhancement. Firstly, the instantaneous frequency of the collected vibration signal is obtained through local maximum synchrosqueezing transformation. Secondly, a local histogram cropping equalization image enhancement algorithm is proposed, which is used to obtain time-frequency images with clearer textures. Then, in order to extract fault features from the enhanced instantaneous frequency (IF) image, A new neural network is proposed. The network consists of Multi-size convolution kernel module, Dual-channel pooling layer and Cross Stage Partial Network (MDCNet). Finally, the fault signal was collected on the bearing fault test bench for prediction, and the accuracy rate reached 99.7%. And compared with AlexNet, VGG-16, Resnet and other methods. The results show that the method can meet the needs of actual engineering.

Low area FPGA implementation of modified histogram estimation architecture with CSAC-DPROM-OBC for medical image enhancement application

Low area FPGA implementation of modified histogram estimation architecture with CSAC-DPROM-OBC for medical image enhancement application

Extending Camera's Capabilities in Low Light Conditions Based on LIP Enhancement Coupled with CNN Denoising.

Using a sensor in variable lighting conditions, especially very low-light conditions, requires the application of image enhancement followed by denoising to retrieve correct information. The limits of such a process are explored in the present paper, with the objective of preserving the quality of enhanced images. The LIP (Logarithmic Image Processing) framework was initially created to process images acquired in transmission. The compatibility of this framework with the human visual system makes possible its application to images acquired in reflection. Previous works have established the ability of the LIP laws to perform a precise simulation of exposure time variation. Such a simulation permits the enhancement of low-light images, but a denoising step is required, realized by using a CNN (Convolutional Neural Network). A main contribution of the paper consists of using rigorous tools (metrics) to estimate the enhancement reliability in terms of noise reduction, visual image quality, and color preservation. Thanks to these tools, it has been established that the standard exposure time can be significantly reduced, which considerably enlarges the use of a given sensor. Moreover, the contribution of the LIP enhancement and denoising step are evaluated separately.

Exploiting Superpixels for Multi-Focus Image Fusion.

Multi-focus image fusion is the process of combining focused regions of two or more images to obtain a single all-in-focus image. It is an important research area because a fused image is of high quality and contains more details than the source images. This makes it useful for numerous applications in image enhancement, remote sensing, object recognition, medical imaging, etc. This paper presents a novel multi-focus image fusion algorithm that proposes to group the local connected pixels with similar colors and patterns, usually referred to as superpixels, and use them to separate the focused and de-focused regions of an image. We note that these superpixels are more expressive than individual pixels, and they carry more distinctive statistical properties when compared with other superpixels. The statistical properties of superpixels are analyzed to categorize the pixels as focused or de-focused and to estimate a focus map. A spatial consistency constraint is ensured on the initial focus map to obtain a refined map, which is used in the fusion rule to obtain a single all-in-focus image. Qualitative and quantitative evaluations are performed to assess the performance of the proposed method on a benchmark multi-focus image fusion dataset. The results show that our method produces better quality fused images than existing image fusion techniques.

A new heterogeneous neural network model and its application in image enhancement

Based on visual cortical theory of Rybak, a new heterogeneous Rybak neural network (HRYNN) model is proposed for image enhancement. HRYNN is constructed with several Rybak neural network (RYNN) models proposed, which have different parameters corresponding to different neurons. We show that HRYNN can better represent prior information for edge detail enhancement than the logarithmic domain. To capture different resolution texture features of image, a novel receptive field model is proposed to solve the problem of detail enhancement. HRYNN model has excellent enhancement effect on the edge details of image based on the receptive field’s lateral inhibitory characteristics. Moreover, the experimental enhancement results of the colour images from Berkeley image Dataset show the validity and efficiency of the proposed enhancement method. Finally, three evaluation indicators are employed to measure the enhancement result.