Image Correction Method Research Articles

P‐20: Image Distortion and Image Correction of Curved OLED Displays

Although flexible and curved OLED displays can bring many novel and interesting applications, their curved structure would also impact optical and image characteristics critical for displays. Here we perform quantitative analyses on geometric distortion and brightness reduction induced by the curved shape and then propose the effective image correction method to re‐construct a proper image for curved OLED displays.

Electricity demand monitoring in Japan by using time-series DMSP stable lights images and its application to long-term damage assessment of natural disasters

This paper demonstrates the potential of the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) stable light imagery for electricity power consumption (EPC) estimation in Japan and long-term damage assessment of natural disasters in term of yearly EPC fluctuation, taking the Japan Earthquake & Tsunami of 2011 as a case study. DMSP stable light data is proved useful for estimation of the economic variables. However, there are several well-known problems with the DMSP including lack of on-board calibration, geometric error, sensor saturation. Many researches have been conducted to develop methods for inter-calibration and saturation correction of DMSP stable light data, but much less effort on geometric correction which should be the first in the pre-processing chain. In term of disaster management, the 2011 Great Earthquake & Tsunami in Japan had severe instant and long-term damages, and DMSP data is potential for long-term damage assessment for the region. In this study, we have: (1) developed a new geometric correction method for DMSP stable light images; (2) developed an EPC estimation method from corrected DMSP data which is then used to derive electricity demand maps for Japan from 1992-2013; (3) performed analysis of correlations between DMSP night light data and GDP and population in Japan; (4) conducted a long-term damage assessment of the Japan Earthquake & Tsunami of 2011 by using the EPC maps in (2). It is found that the proposed geometric correction method gave better correlations between DMSP stable light images of the same year as compared to those of original images and most recent shift-based geometric correction method. Damage assessment reveals that an area of 286.208 km-squared has suffered from the disasters. It caused the EPC of the area drop significantly in 2011 (-42%) and to its lowest level in 2012 (-78%) before starting its recover in 2013.

Diffusion-weighted breast magnetic resonance imaging with distortion correction using non-rigid image registration: a clinical study

Our distortion correction method for diffusion-weighted breast images was applied to clinical studies to evaluate its practicality. We used b values of 0 and 1500s/mm2 and dynamic images captured after injecting the contrast medium for magnetic resonance imaging (MRI). This method is based on non-rigid image registration, and dispenses with multiple scans, reducing examination time and patient burden. Image correction included the following: matrix size matching; segmentation; edge detection; non-rigid image registration between T2-weighted images with fat suppression and b0 images; and image warping of b1500 images. Significant differences were noted for all combinations of cross-correlation coefficients ofwhole-breast images betweenwith and without correction, and no significant differences for apparent diffusion coefficients. The percentage differences were not significant for cross-correlation coefficients, with and without correction, calculated for various sizes, positions, and types of lesions of high-intensity regions. In conclusion, our distortion correction method is effective for clinical breast MRI.

A computer-aided diagnosis system for brain magnetic resonance imaging images using a novel differential feature neural network

To improve the performance of brain tumor diagnosis, numerous automatic brain tumor diagnosis systems that use machine learning technologies have been proposed. However, most current systems ignore the structural symmetry of brain magnetic resonance imaging (MRI) images and regard brain tumor diagnosis as a simple pattern recognition task. As a result, the performance of the current systems is not ideal. To improve the performance of the brain tumor screening process, an innovative differential feature map (DFM) block is proposed to magnify tumor regions, and DFM blocks are further combined with squeeze-and-excitation (SE) blocks to form a differential feature neural network (DFNN). First, an automatic image rectification method is applied so that the symmetry axes of brain MRI images are approximately parallel to the perpendicular axis. Moreover, a DFNN is constructed to classify the brain MRI images into two categories: “abnormal” and “normal”. The experimental results show that the average accuracy of the proposed system on two databases can reach 99.2% and 98%, and the introduction of the proposed DFM block can improve the average accuracy on these two databases by 1.8% and 1.3%, respectively, which indicates that the proposed DFM block can improve the performance of the brain tumor screening process.

Square-Heating Thermography for Quantitative 3D Evaluation of Large Ancient Murals

This work presents recent results of nondestructive health evaluation of the large Buddhist mural “Qifo shuofa tu” (seven Buddha sermon illustration) in the Palace Museum in Beijing, China. In this work, a square-heating thermography system and a thermal tomography method were developed for quantitative 3D evaluation of murals. In particular an image correction method was developed for removing all geometric and illumination distortions. This significantly helped for the seamless combination of all individual images into a single complete image and facilitated quantitative identification and locations of subsurface defects and re-patch pieces from an entire painting image. The thermography results show that this new method has great potential and capability in nondestructive testing and evaluation of large ancient murals.

Improved method for the optical analysis of particulate black carbon (BC) using smartphones

Black carbon (BC) is a major component in atmospheric particulate matter (PM), which causes adverse health impacts and contributes significantly to climate change. Without widespread and accurate BC measurements, it remains difficult to track incomplete combustion sources and reduce BC emissions. Currently commercial BC sensors remain too costly to be deployed widely. In this work, a fast, cost-effective, and easily accessible method based on a smartphone camera was used to quantify color information of PM collected on filters to estimate BC and elemental carbon (EC) loadings. A robust RGB (red, green, blue)-based linear interaction model was built and validated using 1878 PM samples collected in three different regions with collocated BC and EC measurements. After applying image correction methods, this model shows a good predictability with an R-squared (R2) of 0.904 with state-of-the-art BC measurement techniques, and a coefficient of variation of the root mean square error (CV(RMSE)) of 25.3% despite the complex sources and different reference measurement techniques. This work validates the viabilities of using smartphones to quantify BC or EC loading on PM filters with a unified model and track incomplete combustion sources.

A distortion image correction method based on machine vision

As autopilot becomes a hot spot in the field of automobile, machine vision is widely used in autopilot technology, and calibration of camera parameters is the basic work of obtaining driving environment by using vision. In this paper, based on camera parameters and machine vision technology, the monocular camera image distortion correction algorithm is studied. Based on the perspective transformation principle, the method of controlling transformation point is used to correct the image. The black-and-white checkerboard is taken as the object of image acquisition, and the results obtained in this paper are compared with those obtained by the calibration toolbox camera calibrator of matlab camera. The algorithm proposed in this paper is used to correct the images collected in traffic environment, which lays a foundation for the subsequent recognition of traffic signs. The experimental results show that the distorted image correction results are good, the accuracy is higher, and it is practical and effective.

An extensible software platform for interdisciplinary cardiovascular imaging research

Background and objectiveCardiovascular imaging is an exponentially growing field with aspects ranging from image acquisition and analysis to disease characterization, and evaluation of therapy approaches.The transfer of innovative new technological and algorithmic solutions into clinical practice is still slow. In addition to the verification of solutions, their integration in the clinical processing workflow must be enabled for the assessment of clinical impact and risks. The goal of our software platform for cardiac image processing – CAIPI – is to support researchers from different specialties such as imaging physics, computer science, and medicine by a common extensible platform to address typical challenges and hurdles in interdisciplinary cardiovascular imaging research. It provides an integrated solution for method comparison, integrated analysis, and validation in the clinical context. The interface concept enables a combination with existing frameworks that address specific aspects of the pipeline, such as modeling (e.g., OpenCMISS, CARP) or image reconstruction (Gadgetron). MethodsIn our platform, we developed a concept for import, integration, and management of cardiac image data. The integration approach considers the spatiotemporal properties of the beating heart through a specific data model. The solution is based on MeVisLab and provides functionalities for data retrieval and storage. Two types of plugins can be added. While ToolPlugins usually provide processing algorithms such as image correction and segmentation, AnalysisPlugins enable interactive data exploration and reporting. GUI integration concepts are presented for both plugin types. We developed domain-specific reporting and visualization tools (e.g., AHA segment model) to enable validation studies by clinical experts. The platform offers plugins for calculating and reporting quantitative parameters such as cardiac function, which can be used to, e.g., evaluate the effect of processing algorithms on clinical parameters. Export functionalities include quantitative measurements to Excel, image data to PACS, and STL models to modeling and simulation tools. ResultsTo demonstrate the applicability of this concept both for method development and clinical application, we present use cases representing different problems along the innovation chain in cardiac MR imaging.Validation of an image reconstruction method (MRI T1 mapping)Validation of an image correction method for real-time 2D-PC MRIComparison of quantification methods for blood flow analysisTraining and integration of machine learning solutions with expert annotationsClinical studies with new imaging techniques (flow measurements in the carotid arteries and peripheral veins as well as cerebral spinal fluid). ConclusionThe presented platform can be used in interdisciplinary teams, in which engineers or data scientists perform the method validation, followed by clinical research studies in patient collectives. The demonstrated use cases show how it enables the transfer of innovations through validation in the cardiovascular application context.

General Document Image Correction Method Based on Text Line in Natural Scene

This paper presents a general document image correction method based on the text line. The proposed method roughly screens the text area and implements rotation correction based on the TextSnake text detection model. Then by the relative positions of characters, the principle of text direction consistency and the two-way matching strategy, the precise positioning is realized. At last, the information of text ridges is obtained to acquire the nonlinear mapping relationship of the whole graph, Therefore achieving correction reconstruction of the general document image. The experiment results show that the correction method can overall solve image quality problems such as tilt distortion and improve the recognition rate of tilt distortion image.

Backward ray tracing based rectification for real-time integral imaging display system

In an integral imaging (InI) display system, the deviation of the lens position results in blur and distortion of the reconstructed three-dimensional (3D) image. To achieve undistorted and real-time 3D images, an image correction method based on the backward ray tracing (BRT) technology is presented. By finding the correct pixel-to-ray correspondence, the origin and the direction of the light ray for every pixel in elemental image array (EIA) are reset, and the corrected 3D image is obtained based on the BRT technique. Experimental results demonstrate the feasibility and effectiveness of the proposed method, and the structural similarity (SSIM) index of the reconstructed 3D image increased by 20∼30% compared with that without calibrating. The frame rate of EIA generation is over 40 fps, which can realize real-time generation.

Image distortion correction method in a nonuniform temperature field by using Runge-Kutta ray tracing.

To solve the imaging distortion problem in a nonuniform temperature field for visual measurement, this paper studies the Runge-Kutta ray tracing algorithm. First, the light ray emitted by the spatial light point will be abstracted by the proposed method. Then, the ray trajectory in a 3D nonuniform temperature gradient environment will be reproduced, and the ray distortion can be corrected. Finally, the distortion is compared with the actual image distortion obtained through the corner detection method. The experimental results show that the correction method for the distortion image caused by nonuniform temperature gradient heat source interference is feasible by using the Runge-Kutta ray tracing algorithm, which can realize the error compensation for image distortion and obtain an accurate corrected image.

ThermalWrist: Smartphone Thermal Camera Correction Using a Wristband Sensor †

Thermal images are widely used for various healthcare applications and advanced research. However, thermal images captured by smartphone thermal cameras are not accurate for monitoring human body temperature due to the small body that is vulnerable to temperature change. In this paper, we propose ThermalWrist, a dynamic offset correction method for thermal images captured by smartphone thermal cameras. We fully utilize the characteristic that is specific to thermal cameras: the relative temperatures in a single thermal image are highly reliable, although the absolute temperatures fluctuate frequently. To correct the offset error, ThermalWrist combines thermal images with a reliable absolute temperature obtained by a wristband sensor based on the above characteristic. The evaluation results in an indoor air-conditioned environment shows that the mean absolute error and the standard deviation of face temperature measurement error decrease by 49.4% and 64.9%, respectively. In addition, Pearson’s correlation coefficient increases by 112%, highlighting the effectiveness of ThermalWrist. We also investigate the limitation with respect to the ambient temperature where ThermalWrist works effectively. The result shows ThermalWrist works well in the normal office environment, which is 22.91 °C and above.

Accurate binocular stereo underwater measurement method

Light changes its direction of propagation before entering a camera enclosed in a waterproof housing owing to refraction, which means that perspective imaging models in the air cannot be directly used underwater. In this article, we propose an accurate binocular stereo measurement system in an underwater environment. First, based on the physical underwater imaging model without approximation and Tsai’s calibration method, the proposed system is calibrated to acquire the extrinsic parameters, as the internal parameters can be pre-calibrated in air. Then, based on the calibrated camera parameters, an image correction method is proposed to convert the underwater images to air images. Thus, the epipolar constraint can be used to search the matching point directly. The experimental results show that the proposed method in this article can effectively eliminate the effect of refraction in the binocular vision and the measurement accuracy can be compared with the measurement result in air.

A Gray Scale Correction Method for Side-Scan Sonar Images Based on Retinex

When side-scan sonars collect data, sonar energy attenuation, the residual of time varying gain, beam patterns, angular responses, and sonar altitude variations occur, which lead to an uneven gray level in side-scan sonar images. Therefore, gray scale correction is needed before further processing of side-scan sonar images. In this paper, we introduce the causes of gray distortion in side-scan sonar images and the commonly used optical and side-scan sonar gray scale correction methods. As existing methods cannot effectively correct distortion, we propose a simple, yet effective gray scale correction method for side-scan sonar images based on Retinex given the characteristics of side-scan sonar images. Firstly, we smooth the original image and add a constant as an illumination map. Then, we divide the original image by the illumination map to produce the reflection map. Finally, we perform element-wise multiplication between the reflection map and a constant coefficient to produce the final enhanced image. Two different schemes are used to implement our algorithm. For gray scale correction of side-scan sonar images, the proposed method is more effective than the latest similar methods based on the Retinex theory, and the proposed method is faster. Experiments prove the validity of the proposed method.

Adaptive image enhancement method for correcting low-illumination images

In this study, to improve the adaptability of image enhancement in images with low illumination, a colored image correction method based on nonlinear functional transformation according to the illumination-reflection model and multiscale theory is proposed. First, the original RGB image is converted to HSV color space, and the V component is used to extract the illumination component of the scene using the multiscale Gaussian function. Then, a correction function is constructed based on the Weber-Fechner law, and two images are obtained through adaptive adjustments to the image enhancement function parameters based on the distribution profiles of the illumination components. Finally, an image fusion strategy is formulated and used to extract the details from the two images. Compared with the classic algorithm, the proposed algorithm can improve the overall brightness and contrast of an image while reducing the impact of uneven illumination. The enhanced images appear clear, bright, and natural.

Improved Method for Real-time Image Correction Based on the Control Point

Improved Method for Real-time Image Correction Based on the Control Point

Image contrast enhancement method based on display and human visual system characteristics.

At the present time, there are many image contrast enhancement methods where the main considerations are detail enhancement, noise suppression, and high contrast suppression. Traditional methods ignore the characteristics of the display or merely consider the display as a whole. However, due to the limited dynamic range of most display devices on the market, the difference between two adjacent grayscales of the display is often below the just noticeable difference of the human visual systems, which causes many image details to be invisible on the display. To solve this problem, we present a preprocessing method for image contrast enhancement. The method combines the characteristics of the human eye and the display to enhance the image by examining the local histogram. When displaying the processed image, the algorithm maintains as much image information as possible, and image details will not be lost due to the limits of the display device. Moreover, this algorithm performs well for noise suppression and high contrast suppression. The algorithm is an image enhancement method and can also be a correction method for images enhanced by other methods when prepared for display.

RETRACTED ARTICLE: Research on image correction method of network education assignment based on wavelet transform

The network assignment image not only has a sharpness problem in the system audit but also the image angle deviates from the normal angle. Based on this, this study is based on image processing technology, using wavelet transform as the basic algorithm to process the image images collected by the network education system. At the same time, this study extracts the image content by wavelet transform in image edge recognition and enhances the image to enhance the image clarity. For the problem of image tilt, this study proposes a method of detecting the edge of the image with wavelet transform and then performing image tilt correction with Hough transform. In addition, this study compared the performance of the image correction method of this research and the traditional image method through experimental verification. Through comparative analysis, we can know that the performance of this research algorithm is better, and it can provide theoretical reference for subsequent related research.

An efficient motion correction method for frequency-domain images based on Fast Robust Correlation

Motion artifact suppression is an important task in the medical imaging field. Motion during data acquisition can produce blurred images and artifacts. The calculation load for previous motion correction methods is relatively high. In order to decrease computational complexity, an efficient motion correction method is proposed based on fast robust correlation. Fast robust correlation is a computationally efficient search algorithm for translational image matching in the frequency domain. This method calculates the matching surface using a series of high-speed correlations by defining a kernel with sinusoidal terms. The proposed method corrects motion distorted images by aligning translational motion between images formed by neighboring frequency segments. Due to the ineffectiveness of the squared difference kernel to detect motion between partial-Fourier images, the absolute value kernel is proposed, which can be easily approximated by sinusoidal terms. Total variation of the sum of partial-Fourier images is chosen as the new match criterion. FFTs are used to calculate correlations for computational speed. Experimental results show that the proposed method can reduce image motion artifacts effectively and efficiently.

Remote proximity monitoring between mobile construction resources using camera-mounted UAVs

Abstract Struck-by accidents have resulted in a significant number of fatal and nonfatal injuries in the construction industry. As a proactive safety measure against struck-by hazards, the authors present an Unmanned Aerial Vehicle (UAV)-assisted visual monitoring method that can automatically measure proximities among construction entities. To attain this end, this research conducts two research thrusts: (i) object localization using a deep neural network, YOLO-V3; and (ii) development of an image rectification method that allows for the measurement of actual distance from a 2D image collected from a UAV. Tests on real-site aerial videos show the promising accuracy of the proposed method; the mean absolute distance errors for estimated proximity were less than 0.9 m and the mean absolute percentage errors were around 4%. The proposed method enables the advanced detection of struck-by hazards around workers, which in turn can make timely intervention possible. This proactive intervention can ultimately promote a safer working environment for construction workers.