Image Quality Evaluation Method Research Articles

A Time–Frequency Image Quality Evaluation Method Based on Improved LIME

Deep learning has the characteristics of high efficiency, high accuracy and low knowledge dependence, and has become a hot method in the research field of rotating machinery fault diagnosis. The time–frequency transform can show both the time and frequency characteristics of the vibration signal, so the time–frequency image is often used as the input of deep learning networks. At present, there are many time–frequency transform methods, and how to choose one is a problem worth discussing. This paper proposed a time–frequency image quality evaluation method based on improved local interpretable model-agnostic explanations (LIME). With the input of deep learning networks as the application background, this method evaluates the time–frequency image quality of rotating machinery vibration signals from two aspects: the accuracy of the diagnosis results and the consistency of the interpretation results with prior knowledge. The feasibility of the proposed evaluation method is verified by experiments on the measured data set, and engineers’ trust in the deep learning model is improved.

Underwater image classification based on image enhancement and information quality evaluation

Underwater target imaging is widely used in oceans, rivers and lakes detection fields, but due to the existence of water on light absorption scattering attenuation effect, the diffraction limit of imaging system, aberration distortion and underwater turbulence, underwater images has serious degradation, mainly manifested in noise, fuzzy and low resolution, etc. In recent years, some scholars have started from the image enhancement and restoration to improve the image degradation and improve the image clarity, but the effect still needs to be improved, and the resolution can not be improved, and some algorithms will even reduce the resolution.Resolution is an important parameter for the image evaluation, improving the image quality in the underwater imaging, especially in the human eye vision, which is largely determined by the improved resolution. Therefore, this paper starts from each aspects of image processing, studies, proposes and experiments a large number of image recovery algorithms. This paper analyzes various factors introducing noise in underwater imaging process, proposes block mixed filter image denoising method based on noise statistical characteristics, analyzes the necessity of evaluating image quality by using objective indicators, and gives the corresponding objective evaluation parameters according to the characteristics of the image recovery algorithm. According to the noise preprocessing method and the image quality evaluation method, the underwater image synthesis dataset is screened to improve the performance of the underwater object recognition model.

Image quality evaluation method and system for head-mounted three-dimensional display

Image quality evaluation method and system for head-mounted three-dimensional display

Blind Image Quality Evaluation Method Based on Cyclic Generative Adversarial Network

Blind Image Quality Evaluation Method Based on Cyclic Generative Adversarial Network

Low-dose CT image quality evaluation method based on radiomics and deep residual network with attention mechanism

Low-dose computed tomography (CT) produces less radiation but may degrade image quality. Research on automatic evaluation and classification of low-dose CT image quality is necessary, which is helpful to clinicians. A low-dose CT image quality evaluation method is proposed based on radiomics and deep learning. After pre-training on the ImageNet dataset, the trained deep learning model is then transferred to the target dataset of low-dose CT image. Region of interest is obtained through a deep residual network with attention mechanism. Pyradiomics is used to extract radiomics features of ROI, and T-test and LASSO algorithm are used for feature screening. Finally, support vector machine, random forest and logistic regression models are adopted to classify and predict the quality of low-dose CT image based on the selected radiomics features. Experimental results show that the trained deep residual network model with attention module can achieve 92% recognition accuracy of region of interest, and the evaluation accuracy of the three machine learning models achieve 86.6% (support vector machine), 84.5% (random forest) and 61.7% (logistic regression), respectively. Transfer learning can effectively reduce the required number of samples for the training of deep learning model and avoid overfitting. After the introduction of attention module in deep residual network model, accuracy rises faster, and the final accuracy is slightly higher than that of deep residual network model without attention module, but the improvement is slightly lower than expected. Radiomics features of region of interest reflect the quality differences of low-dose CT images which can be employed for the quality evaluation of low-dose CT image.

A Survey of Deep Learning-Based Low-Light Image Enhancement.

Images captured under poor lighting conditions often suffer from low brightness, low contrast, color distortion, and noise. The function of low-light image enhancement is to improve the visual effect of such images for subsequent processing. Recently, deep learning has been used more and more widely in image processing with the development of artificial intelligence technology, and we provide a comprehensive review of the field of low-light image enhancement in terms of network structure, training data, and evaluation metrics. In this paper, we systematically introduce low-light image enhancement based on deep learning in four aspects. First, we introduce the related methods of low-light image enhancement based on deep learning. We then describe the low-light image quality evaluation methods, organize the low-light image dataset, and finally compare and analyze the advantages and disadvantages of the related methods and give an outlook on the future development direction.

Research on image quality evaluation and its application in image fusions

In view of the digital image in the process of acquisition, storage, processing and transmission due to the camera equipment, compression degree and transmission bandwidth caused by various distortion problems, this paper summarizes the image quality evaluation methods. Firstly, two methods of image quality evaluation, namely subjective evaluation and objective evaluation, are introduced, and then the relationship between subjective evaluation and objective evaluation is analyzed. Finally, the image quality evaluation method is successfully applied to the image fusion processing test, which provides ideas for the subsequent research on image quality evaluation methods.

A novel image quality evaluation method based on two-dimensional information entropy

In image transmission, with the consideration of system parameters, such as bandwidth, transmission delay and power consumption, the image compression coding is often used to reduce the amount of transmitted data. The formation mechanism of block effect in H.264 coding is analyzed. Taking H.264 coding as an example, the spatial domain image enhancement processing is adopted in the paper to obtain the two-dimensional information entropy covering the gray correlation and image edge feature information in adjacent spatial domain. By referring to the two-dimensional information entropy evaluation value of original and coded image, the image quality evaluation result is consistent with PSNR method used in common situations. Compared with the traditional PSNR evaluation method, the two-dimensional information entropy evaluation method only refers to the two-dimensional information entropy evaluation value of the original image, and the amount of data required is very low. Therefor, it is more suitable for the engineering implementation of real-time image quality evaluation in image transmission system. At the same time, with the edge feature information of the image used in the two-dimensional information entropy, the entropy method is closer to the actual feeling of human eyes than PSNR method.

Finding the Least Motion-Blurred Image by Reusing Early Features of Object Detection Network

Taking smartphone-made videos for photogrammetry is a convenient approach because of the easy image collection process for the object being reconstructed. However, the video may contain a lot of relatively similar frames. Additionally, frames may be of different quality. The primary source of quality variation in the same video is varying motion blur. Splitting the sequence of the frames into chunks and choosing the least motion-blurred frame in every chunk would reduce data redundancy and improve image data quality. Such reduction will lead to faster and more accurate reconstruction of the 3D objects. In this research, we investigated image quality evaluation in the case of human 3D head modeling. Suppose a head modeling workflow already uses a convolutional neural network for the head detection task in order to remove non-static background. In that case, features from the neural network may be reused for the quality evaluation of the same image. We proposed a motion blur evaluation method based on the LightGBM ranker model. The method was evaluated and compared with other blind image quality evaluation methods using videos of a mannequin head and real faces. Evaluation results show that the developed method in both cases outperformed sharpness-based, BRISQUE, NIQUE, and PIQUE methods in finding the least motion-blurred image.

Image Quality Evaluation Method for Optical Remote Sensing Satellite Based on an Array of Point Sources

Image Quality Evaluation Method for Optical Remote Sensing Satellite Based on an Array of Point Sources

A Review of Image Super-Resolution Approaches Based on Deep Learning and Applications in Remote Sensing

At present, with the advance of satellite image processing technology, remote sensing images are becoming more widely used in real scenes. However, due to the limitations of current remote sensing imaging technology and the influence of the external environment, the resolution of remote sensing images often struggles to meet application requirements. In order to obtain high-resolution remote sensing images, image super-resolution methods are gradually being applied to the recovery and reconstruction of remote sensing images. The use of image super-resolution methods can overcome the current limitations of remote sensing image acquisition systems and acquisition environments, solving the problems of poor-quality remote sensing images, blurred regions of interest, and the requirement for high-efficiency image reconstruction, a research topic that is of significant relevance to image processing. In recent years, there has been tremendous progress made in image super-resolution methods, driven by the continuous development of deep learning algorithms. In this paper, we provide a comprehensive overview and analysis of deep-learning-based image super-resolution methods. Specifically, we first introduce the research background and details of image super-resolution techniques. Second, we present some important works on remote sensing image super-resolution, such as training and testing datasets, image quality and model performance evaluation methods, model design principles, related applications, etc. Finally, we point out some existing problems and future directions in the field of remote sensing image super-resolution.

Multi-factor fusion index assessment of digital speckle interferometry images quality under dynamic environment

When digital speckle pattern interferometry (DSPI) is applied to dynamic environmental, it is often accompanied by severe speckle de-correlation, which severely limits the application and detection accuracy of DSPI. Currently, the existing speckle image quality evaluation methods only consider the contrast of the speckle image or the morphology of speckle particles, which results in these methods unapplicable to dynamic environments. For this reason, this paper proposes a new evaluation method for laser speckle image, namely dynamic multi-factor fusion index (DMFFI), which combines evaluation of four aspects: speckle stability, speckle resolution, uniformity of gray distribution, and contrast. And then, the DMFFI method is compared with the traditional evaluation methods including mean gray gradient and second order derivative of mean gray gradient. The experimental results show that DMFFI has a better ability to evaluate the quality of laser speckle images under dynamic environment compared with the traditional evaluation methods. Finally, a comparison test under different environments is conducted, and the effects of environments on the quality of speckle images are analyzed and discussed, and a guideline for speckle image acquisition in DSPI technique is proposed, which have great significance and value for the application of DSPI technique to dynamic environment detection.

Remote Sensing Image Information Quality Evaluation via Node Entropy for Efficient Classification

Combining remote sensing images with deep learning algorithms plays an important role in wide applications. However, it is difficult to have large-scale labeled datasets for remote sensing images because of acquisition conditions and costs. How to use the limited acquisition budget to obtaina better remote sensing image dataset is a problem worth studying. In response to this problem, this paper proposes a remote sensing image quality evaluation method based on node entropy, which can be combined with active learning to provide low-cost guidance for remote sensing image collection and labeling. The method includes a node selection module and a remote sensing image quality evaluation module. The function of the node selection module is to select representative images, and the remote sensing image quality evaluation module evaluates the remote sensing image information quality by calculating the node entropy of the images. The image at the decision boundary of the existing images has a higher information quality. To validate the method proposed in this paper, experiments are performed on two public datasets. The experimental results confirm the superiority of this method compared with other methods.

Fully automated image quality evaluation on patient CT: Multi-vendor and multi-reconstruction study.

While the recent advancements of computed tomography (CT) technology have contributed in reducing radiation dose and image noise, an objective evaluation of image quality in patient scans has not yet been established. In this study, we present a patient-specific CT image quality evaluation method that includes fully automated measurements of noise level, structure sharpness, and alteration of structure. This study used the CT images of 120 patients from four different CT scanners reconstructed with three types of algorithm: filtered back projection (FBP), vendor-specific iterative reconstruction (IR), and a vendor-agnostic deep learning model (DLM, ClariCT.AI, ClariPi Inc.). The structure coherence feature (SCF) was used to divide an image into the homogeneous (RH) and structure edge (RS) regions, which in turn were used to localize the regions of interests (ROIs) for subsequent analysis of image quality indices. The noise level was calculated by averaging the standard deviations from five randomly selected ROIs on RH, and the mean SCFs on RS was used to estimate the structure sharpness. The structure alteration was defined by the standard deviation ratio between RS and RH on the subtraction image between FBP and IR or DLM, in which lower structure alterations indicate successful noise reduction without degradation of structure details. The estimated structure sharpness showed a high correlation of 0.793 with manually measured edge slopes. Compared to FBP, IR and DLM showed 34.38% and 51.30% noise reduction, 2.87% and 0.59% lower structure sharpness, and 2.20% and -12.03% structure alteration, respectively, on an average. DLM showed statistically superior performance to IR in all three image quality metrics. This study is expected to contribute to enhance the CT protocol optimization process by allowing a high throughput and quantitative image quality evaluation during the introduction or adjustment of lower-dose CT protocol into routine practice.

Appearance Fault Diagnosis of a Transformer Based on Data Set Optimization and Heterogeneous Model Ensemble

Prosumers refer to the integration of production and consumption. Due to a large number of access to distributed power sources, electric vehicles, etc., which have a certain impact on power transformers, and increasing potential failures, transformers need to be monitored. In recent years, image recognition technology based on deep learning has been widely used in intelligent inspection image analysis. Aiming at the problem that the accuracy of appearance fault diagnosis in intelligent inspection images is limited by image quantity and quality, an image data set optimization method based on a seamless cloning algorithm and image cleaning is proposed. First, a sample generation method based on the seamless fusion algorithm is proposed, which seamlessly fuses the corroded texture of other power equipment into the transformer image to generate the rust transformer image. On this basis, an image quality evaluation and screening method based on the XGBoost algorithm is proposed to evaluate the image quality of the data set and clean the low-quality images. In addition, aiming at the limitation of a single diagnosis algorithm, an appearance diagnosis method based on heterogeneous model ensemble learning is proposed. By constructing multiple learning models and using a weighted voting strategy to fuse model outputs as final outputs, the accuracy of fault diagnosis is improved.

Efficient multiplexed illumination and imaging approach for Fourier ptychographic microscopy.

A Fourier ptychographic microscope (FPM) can obtain images with high resolution and a wide field of view (FOV). However, the time-consuming process of image acquisition and computation leads to low reconstruction efficiency. Therefore, we propose a state-multiplexed method through an optimized illumination pattern to accelerate FPM. First, to reduce the redundancy of the spectrum and analyze the impact of missing a certain sub-spectrum on overall spectrum reconstruction, we use an image quality evaluation method to obtain the differential expression between missing a certain LED lighting and all LED lighting. Second, we use the difference expression to select the important LEDs and obtain an optimized illumination pattern that lights up only the LEDs in the central area and the edge LEDs. Then, we update the multiplexing method with the new, to the best of our knowledge, illumination pattern and obtain satisfactorily reconstructed images. Finally, we validate the effectiveness and efficiency of our method with both simulation and experiments. Compared with the traditional method, our method accelerates the reconstruction speed of FPM while ensuring a large FOV and high resolution, saving about 73% of time.

Multitopic underwater image quality assessment with visual attention factors

Statistical information on underwater images related to subjective quality perception differs based on the environment. We initially propose whitish, yellowish, greenish, bluish topics for underwater images obtained by applying an integrated color distribution, that is, images under the same topic have the same hue. Inspired by the local multiscale, multidirectional statistical characteristics of the structure, contrast, and color of underwater images, we constructed visual attention factor sets for the above four typical underwater environment topics. Furthermore, a multitopic underwater image quality evaluation (MTUIQE) model is proposed to measure mix distortions in different underwater images. The method of measuring image quality based on image topic is proven to be more effective than directly fitting all the natural scene statistics features to the mean opinion scores. Experiments were conducted to show that the proposed MTUIQE approach is superior to existing objective underwater image quality evaluation methods. Compared with six commonly used blind image quality evaluation methods, the Pearson linear correlation coefficient and Spearman rank-order correlation coefficient are increased by more than 10%, which means that the quality assessment model adapted to specific degradation topics is more consistent with subjective underwater image quality perception, and it can effectively evaluate the performance of underwater image enhancement and restoration methods.

A high compatibility finger vein image quality assessment system based on deep learning

Finger vein feature recognition has been widely studied due to its high security and stability. However, the systematic errors caused by false and missing information in low-quality finger vein images are still concerns. Recently, researchers have proposed a finger vein image quality assessment scheme to eliminate low-quality images. The major drawback of this strategy is the dependence on limited domain knowledge as the strategy cannot effectively filter out low-quality images and is only adapted to a certain recognition method or database. In this paper, we design a new finger vein image quality evaluation method driven by improving the recognition performance of the recognition system and based on the possibility of the false rejection of low-quality images. The method uses statistical knowledge to analyse the matching of different samples between fingers so as to automatically determine the image quality and classify it. Then, we further design a lightweight convolutional neural network to train the high-quality and low-quality images obtained by the above evaluation criteria and mine the common attributes between low-quality images so as to ensure that the prediction system can judge the image quality quickly and accurately in practical use. The proposed scheme is validated on multiple public datasets and compared with current recognition algorithms. The experimental results show that the quality evaluation standard proposed in this paper has good universality and can be combined with different types of recognition methods and acquisition equipment to well screen out the existing low-quality images. Furthermore, the prediction method proposed in this paper has achieved excellent prediction performance on three public datasets, showing that the proposed method can effectively improve the performance of the original recognition system. Taking the local binary pattern (LBP) algorithm as an example, after using the prediction algorithm proposed in this paper to eliminate low-quality images, the recognition system improves the original recognition performance on the three datasets by 12.670% (SDUMLA), 11.923% (MMCBNU_6000) and 9.940% (FV-USM).

D2BGAN: A Dark to Bright Image Conversion Model for Quality Enhancement and Analysis Tasks Without Paired Supervision

This paper presents an image enhancement model, D2BGAN (Dark to Bright Generative Adversarial Network), to translate low light images to bright images without a paired supervision. We introduce the use of geometric and lighting consistency along with a contextual loss criterion. These when combined with multiscale color, texture and edge discriminators prove to provide competitive results. We performed extensive experiments using benchmark datasets to visually and objectively compare our results. We observed the performance of D2BGAN on real-time driving datasets that are subject to motion blur, noise, and other artifacts. We further demonstrated that our enhanced images can be profitably used in image-understanding tasks. Images processed using our technique obtain the best or second best average scores for three different image quality evaluation methods on the Naturalness Preserved Enhancement (NPE), Low Light Image Enhancement (LIME), Multi-Exposure Image Fusion (MEF) benchmark datasets. Best scores are also obtained on the LOw-Light (LOL) test set and on Berkeley Driving Dataset (BDD) images processed with D2BGAN. Face detection tasks on the DarkFace benchmark dataset show an mAP (mean Average Precision) improvement from 0.209 to 0.301 when images are processed using D2BGAN. mAP further improves to 0.525 when finetuning techniques are adopted.

Restoration of Laser Interference Image Based on Large Scale Deep Learning

Although the deep learning method has achieved some results in the challenging task of repairing the missing areas of images over the years, there is no report on the images disturbed by the laser entering the field of view. We propose a restoration model that can restore the laser interference image, the corresponding countermeasure network deep learning model and a new model training method, and no additional manual marking work is required in the data training set. After a large number of experiments, the loss function can rapidly converge under this method, accurately give the reasonable reconstruction results of the image disturbed by laser, and significantly improve the scores of many common image quality evaluation methods: high-quality repair results are obtained on the laser interference composite dataset of face (CelebA), Stanford automobile, aircraft, buildings (Facade) and satellite images. It has the characteristics of fast training speed, strong robustness, modular model design and wide application.