Blur Correction Research Articles

Super-resolution imaging through scattering media based on improved triple correlation recursion and deterministic iterative estimation.

Iterative phase retrieval algorithms are commonly used in computational techniques and optimization methods to obtain the reconstruction of objects hidden behind opaque scattering media. However, these methods are susceptible to converging to incorrect local minima, and the calculation results tend to be unstable. In this paper, a triple-correlation-based super-resolution imaging (TCSI) framework is proposed to achieve single-shot imaging of unknown objects hidden behind the scattering medium. The amplitude spectrum of the object is obtained by a speckle correlation (SC) method. Iterative relaxation recursion (IRR) sufficiently extracts object information from the triple correlation (TC) of the speckle patterns, serving as the prior initial guess for the iterative estimation algorithm (IE) to obtain a deterministic phase spectrum. Blur correction (BC) is then applied to the diffraction-limited image to achieve super-resolution imaging. Experimental results demonstrate that the flexible framework could effectively overcome the influence of speckle resolution and outperform traditional methods in terms of performance. Our approach provides a basis for non-invasively visualizing various samples behind scattering media.

Hybrid 5G Positioning Systems with Applications to Mobile Robot Technology

A new concept of 5G/INS integration of artificial intelligence-based systems is proposed, namely the Recurrent Neural Networkbased Fuzzy Inference System (RNNFIS). The 5G system with fuzzy logic was used as a reference during its availability. Data from the 5G system and the inertial navigation system (INS) are used to build a structured knowledge base consisting of the behavior of the INS system in some special scenarios of robotic technology. Using the same data, the proposed fuzzy system is trained to obtain corrected navigation data. In the absence of 5G system information, the system will only utilize data from the INS system and blur correction algorithms to perform its tasks.

Correction of rotational blur in images of stars observed by an astroinertial attitude sensor against the background of the daytime sky

A rotational blur correction algorithm is considered as the initial stage of image processing in the problem of attitude measurement using a star tracker. To implement this algorithm, the star tracker must be equipped with a three-axis gyroscope. The algorithm does not guarantee the detection of an image of a star against the background of the daytime sky in one frame but facilitates conditions for subsequent image stacking. The correction aims to localize energy maxima of the blurred star images in pixels with predetermined characteristics. The correction highlights these pixels against the background and improves the signal-to-noise ratio, though deteriorating the artistic quality of the whole digital image. The key characteristic of the pixel of maximum localization is that it is where the geometric image of the star is found at the start of the exposure of the frame under correction. The correction is performed in the form of frame processing with a digital finite-impulse-response (FIR) filter. The impulse response of the filter is inhomogeneous and represents a core of rotational blur, synthesized in each pixel of the corrected frame. Algorithms for calculating levels of the signal, background, and noise in the image of a star observed against the background of the daytime sky with a rotating camera are described. Dependences of the signal-to-noise ratios in various pixels of a blurred image on the exposure time and on the angular velocity of the camera rotation are analyzed. The signal-to-noise ratios in the star image before and after the blur correction are calculated. The simulation results are illustrated by the example of an image of a bright star, clearly showing specific features of the proposed rotational blur correction algorithm.

Optimization of blur correction of color images formed with aerial imaging by retro-reflection

Optimization of blur correction of color images formed with aerial imaging by retro-reflection

Tunnel Lining Surface Monitoring System Deployable at Maximum Vehicle Speed of 100 km/h Using View Angle Compensation Based on Self-Localization Using White Line Recognition

Vehicle-mounted inspection systems have been developed to perform daily inspections of tunnel lining. However, it is difficult to continuously scan images at the desired target angle with the camera’s limited visual field because the vehicle does not always maintain the same position in the car lane while it runs. In this study, we propose a calibration-free optical self-localization method based on white line recognition, which can stabilize the scanning angle and thus enable the scanning of the entire tunnel lining surface. Self-localization based on the present method has a processing time of 27.3 ms, making it valid for real-time view angle compensation inside tunnels, where global positioning systems cannot operate. It is robust against changes in brightness at tunnel entrances and exits and capable of dealing with car lanes with dotted lines and regular white lines. We confirmed that it is possible to perform self-localization with 97.4% accuracy using the scanned data of white lines captured while the vehicle is running. When this self-localization was combined with an inspection system capable of motion blur correction, we were able to sequentially scan the tunnel lining surface at a stable camera angle at high speed, and it was shown that the acquired images could be stitched together to produce an expansion image that can be used for inspection.

Computer Image Content Retrieval considering K-Means Clustering Algorithm

The traditional computer image content retrieval technology can only meet the specific requirements of customers; because of its general features, it cannot comply with the requirements of various environments, purposes, and time simultaneously. This study presents a computer image content retrieval method for a K-means clustering algorithm (KCA). The information collected by computer is preprocessed by K-means clustering algorithm, and the unacquired computer image is labeled based on the optimal learning order according to the KCA. The K-means clustering algorithm classifies the color, pattern, shape, and content of computer images and takes advantage of the invariance advantages of image content retrieval such as scale, rotation, illumination, and blur correction to effectively solve the recurring problems of computer images during retrieval and increase its accuracy. The results of the experiment indicate that the proposed K-means clustering algorithm can enhance the efficiency and performance effectively during image retrieval by computer as compared to the traditional content search algorithm and also help to quickly converge to the query content; it also shows that KCA has good robustness.

Estimation and correction of motion blur in digital images

Digital images play a very important role in developing computer-aided systems. The motion blur and blur in such types of images affect the accuracy of the system. Therefore, it is a challenging task to estimate and remove the blur in the images. In the present paper, an attempt is made to use a Convolutional Neural Network (CNN) model to estimate and remove the blur in the images. The CNN model with different functions helps to improve the accuracy of removing blur from the images. Different network functions, such as ReLU and Sigmoid, and their combinations are analyzed for the modeling of CNN. The performance of CNN is analyzed with different parameters, such as blur estimation, PSNR, RMSE, SSIM, and MSE. The performance is measured by considering different image categories, such as more blur images, less blur images, dark blur images, and biomedical images. Considering the parameters, it is observed that CNN with ReLU and Sigmoid functions is giving better performance than other network functions. It is observed that CNN models are giving successful performance to remove blur and correct the blur than any other traditional models.

Investigation of the impact of blur under mobile attentional orientation using a vision simulator.

It is well-known that correction of blur can improve visual perception. However, it is unclear how the beneficial effect of correction is affected by the regions of correction and the spatial uncertainty introduced by the retinal stimulation. The purpose of this study was two-fold: first, to compare the impacts of blur correction between isoeccentric locations of the visual field; and second, to evaluate the effect of spatial cueing in each corrected location on performing a simple task. Five subjects were asked to complete a simple detection task of a small dark spot stimulus presented randomly at four cardinal retinal locations (eccentricity: 5°) under manipulation of attention via an exogenous cue. Both clear and blurred targets were randomly displayed across the visual field and viewed monocularly through a vision simulator, used to minimize peripheral ocular aberrations. Results confirmed the advantage of clear vs/ blurred images under spatial uncertainty. It was also found that the visual benefit from blur correction is unequal at isoeccentric locations, even for a simple detection task. While manipulation of attention in the presence of spatial uncertainty significantly modulated response time (RT) performance, no differential effect was observed for clear and blurred stimuli, suggesting that attention has only a small effect on the optical benefit for a simple detection task when the display is depleted (no distractor). Those observations highlight the importance of field performance asymmetries in optical interventions and may offer useful implications for the design of extrafoveal refractive correction. Further studies are needed to elucidate how the focus of attention interacts with the perceived gain of vision correction.

Multiplanar full-field blur correction method for infrared microscopy imaging.

We propose a 3D full-field focusing method for microscopic mid-wave infrared (MWIR) imagery. The method is based on the experimental estimation of a confined volumetric vision microscope point spread function. The technique employs our well-known constant-range-based nonuniformity correction algorithm as a preprocessing step and then an iteration in the z-axis Fourier-based deconvolution. The technique's ability to compensate for localized blur is demonstrated using two different real MWIR microscopic video sequences, captured from two microscopic living organisms using a Janos-Sofradir MWIR microscopy setup. The performance of the proposed algorithm is assessed on real and simulated noisy infrared data by computing the root-mean-square error and the roughness Laplacian pattern indexes, which are specifically developed for the present work.

RETRACTED ARTICLE: Conventional neural network for blind image blur correction using latent semantics

In this work, deep learning for enhancing the sharpness of blurred image is investigated. Initial pre-processing is blur image kernel estimation which is critical for blind image de-blurring. In prior investigation, handcrafted blur features are optimized for certain uniform blur, which is unrealistic for blind de-convolution. To deal with this crisis, initially this work attempts to carry out kernel matrix estimation using latent semantic analysis (KME-LSA) in dermatology image. In order to enhance the image sparseness, this work modelled an image descriptor based on Gaussian mixture model in auto-encoder (GMM-AE) as a primary layer in convolutional neural networks. The functionality of the proposed GMM-AE triggers the selection of efficient features for subsequent layers in CNN. The features extracted from the integrated trained GMM-AE in CNN can fine-tune the quality of blurred image. Datasets used are melanoma-based dermascope images. Pre-processing procedures are carried out by LSA-based kernel matrix estimation. The attained sharp image outcome is given to the proposed model for effective feature extraction and to attain improved blind image. The anticipated KME-LSA and GMM-AE in CNN estimates blur parameters with high accuracy. Experiment illustrates the efficacy of proposed method and the competitive outcomes are compared with state-of-the-art datasets. Simulation was carried out in MATLAB environment; performance metrics like MSE—227.6, PSNR—33.6762, SSIM—0.9755 and VIF—0.08162 are evaluated. The results show better trade-off than the prevailing techniques.

Correcting image blur in spiral, retraced in/out (RIO) acquisitions using a maximized energy objective.

Images acquired with spiral k-space trajectories can suffer from off-resonance image blur. Previous work showed that averaging 2 images acquired with a retraced, in/out (RIO) trajectory self-corrects image blur so long as off-resonant spins accrue less than 1 half-cycle of relative phase over the readout. Practical scenarios frequently exceed this threshold. Here, we derive and characterize a more-robust off-resonance image blur correction method for RIO acquisitions. Phantom and human volunteer data were acquired using a RIO trajectory with readout durations ranging from 4 to 60 ms. The resulting images were deblurred using 3 candidate methods: conventional linear correction of the component images; semiautomatic deblurring of the component images using an established minimized phase objective function; and semiautomatic deblurring of the average of the component images using a maximized energy objective function, derived below. Deblurring errors were estimated relative to images acquired with 4 ms readouts. All 3 methods converged to similar solutions in cases where less than 2 and 4 cycles of phase accrued over the readout in in vivo and phantom images, respectively (<13 ms readout at 3T). Above this threshold, the linear and minimized phase methods introduced several errors. The maximized energy function provided accurate deblurring so long as less than 6 and 10 cycles of phase accrued over the readout in in vivo and phantom images, respectively (<34 ms readout at 3T). The maximized energy objective function can accurately deblur RIO acquisitions over a wide spectrum of off resonance frequencies.

Application of side-oblique image-motion blur correction to Kuaizhou-1 agile optical images.

Given the recent development of agile optical satellites for rapid-response land observation, side-oblique image-motion (SOIM) detection and blur correction have become increasingly essential for improving the radiometric quality of side-oblique images. The Chinese small-scale agile mapping satellite Kuaizhou-1 (KZ-1) was developed by the Harbin Institute of Technology and launched for multiple emergency applications. Like other agile satellites, KZ-1 suffers from SOIM blur, particularly in captured images with large side-oblique angles. SOIM detection and blur correction are critical for improving the image radiometric accuracy. This study proposes a SOIM restoration method based on segmental point spread function detection. The segment region width is determined by satellite parameters such as speed, height, integration time, and side-oblique angle. The corresponding algorithms and a matrix form are proposed for SOIM blur correction. Radiometric objective evaluation indices are used to assess the restoration quality. Beijing regional images from KZ-1 are used as experimental data. The radiometric quality is found to increase greatly after SOIM correction. Thus, the proposed method effectively corrects image motion for KZ-1 agile optical satellites.

UAV IMAGE BLUR – ITS INFLUENCE AND WAYS TO CORRECT IT

Abstract. Unmanned aerial vehicles (UAVs) have become an interesting and active research topic in photogrammetry. Current research is based on image sequences acquired by UAVs which have a high ground resolution and good spectral resolution due to low flight altitudes combined with a high-resolution camera. One of the main problems preventing full automation of data processing of UAV imagery is the unknown degradation effect of blur caused by camera movement during image acquisition. The purpose of this paper is to analyse the influence of blur on photogrammetric image processing, the correction of blur and finally, the use of corrected images for coordinate measurements. It was found that blur influences image processing significantly and even prevents automatic photogrammetric analysis, hence the desire to exclude blurred images from the sequence using a novel filtering technique. If necessary, essential blurred images can be restored using information of overlapping images of the sequence or a blur kernel with the developed edge shifting technique. The corrected images can be then used for target identification, measurements and automated photogrammetric processing.

Beat Detection of Blood Vessel from SLO Eye Fundus Moving Image

Abstract An eye fundus is an only part where a blood vessel can be observed directly from the outside of the human body. An elasticity of a blood vessel on an eye fundus is lost according as the progression of arteriosclerosis. The elasticity can be evaluated by observing the beating of the blood vessel. We propose in this paper a method of a beat detection of blood vessel from SLO (Scanning Laser Opthalmoscope) eye fundus moving image. The proposed method consists of a blur correction and a vessel diameter evaluation. In the experiments, the beating of the blood vessel has been successfully detected from the SLO moving image.

Influence of blur on feature matching and a geometric approach for photogrammetric deblurring

Abstract. Unmanned aerial vehicles (UAV) have become an interesting and active research topic for photogrammetry. Current research is based on images acquired by a UAV, which have a high ground resolution and good spectral and radiometric resolution, due to the low flight altitudes combined with a high resolution camera. UAV image flights are also cost efficient and have become attractive for many applications including change detection in small scale areas. One of the main problems preventing full automation of data processing of UAV imagery is the degradation effect of blur caused by camera movement during image acquisition. This can be caused by the normal flight movement of the UAV as well as strong winds, turbulence or sudden operator inputs. This blur disturbs the visual analysis and interpretation of the data, causes errors and can degrade the accuracy in automatic photogrammetric processing algorithms. The aim of this research is to develop a blur correction method to deblur UAV images. Deblurring of images is a widely researched topic and often based on the Wiener or Richardson-Lucy deconvolution, which require precise knowledge of both the blur path and extent. Even with knowledge about the blur kernel, the correction causes errors such as ringing, and the deblurred image appears "muddy" and not completely sharp. In the study reported in this paper, overlapping images are used to support the deblurring process, which is advantageous. An algorithm based on the Fourier transformation is presented. This works well in flat areas, but the need for geometrically correct sharp images may limit the application. Deblurring images needs to focus on geometric correct deblurring to assure geometric correct measurements.

両面印刷物のスキャナ画像の裏写りとボケ補正

両面印刷物のスキャナ画像に生じた裏写りとボケを補正する手法を提案する．紙内部での光の拡散を表面，裏面の双方向について考慮することで，裏写りだけでなく表面のパターンに生じるボケも補正できる．光の拡散を表すPSFと裏写り補正画像はコスト関数を用いた最適化により同時に推定される．コスト関数で正規化項として利用される重み付きのトータルバリエーション（TV）を裏写りの度合いを自動評価する指標として利用することで，パラメータを自動的に最適化し補正結果を改善した．実験により，両面に印刷がある場合の裏写りを良好に補正できること，および裏写りが印刷のある場所にしか生じていない場合にも補正できることを確認した．また，ボケを補正する効果も確認できた．PSFを最小二乗法で推定する手法とガウス分布型PSFを用いる2通りの手法について比較したところ，今回の実験では後者の方が良好な結果が得られた．

DETECTING AND CORRECTING MOTION BLUR FROM IMAGES SHOT WITH CHANNEL-DEPENDENT EXPOSURE TIME

Abstract. This article describes a pipeline developed to automatically detect and correct motion blur due to the airplane motion in aerial images provided by a digital camera system with channel-dependent exposure times. Blurred images show anisotropy in their Fourier Transform coefficients that can be detected and estimated to recover the characteristics of the motion blur. To disambiguate the anisotropy produced by a motion blur from the possible spectral anisotropy produced by some periodic patterns present in a sharp image, we consider the phase difference of the Fourier Transform of two channel shot with different exposure times (i.e. with different blur extensions). This is possible because of the deep correlation between the three visible channels ensures phase coherence of the Fourier Transform coefficients in sharp images. In this context, considering the phase difference constitutes both a good detector and estimator of the motion blur parameters. In order to improve on this estimation, the phase difference is performed on local windows in the image where the channels are more correlated. The main lobe of the phase difference, where the phase difference between two channels is close to zero actually imitates an ellipse which axis ratio discriminates blur and which orientation and minor axis give respectively the orientation and the blur kernel extension of the long exposure-time channels. However, this approach is not robust to the presence in the phase difference of minor lobes due to phase sign inversions in the Fourier transform of the motion blur. They are removed by considering the polar representation of the phase difference. Based on the blur detection step, blur correction is eventually performed using two different approaches depending on the blur extension size: using either a simple frequency-based fusion for small blur or a semi blind iterative method for larger blur. The higher computing costs of the latter method make it only suitable for large motion blur, when the former method is not applicable.

Combined correction of recovery effect and motion blur for SUV quantification of solitary pulmonary nodules in FDG PET/CT

We evaluate a fully data-driven method for the combined recovery and motion blur correction of small solitary pulmonary nodules (SPNs) in F-18 fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT). The SPN was segmented in the low-dose CT using a variable Hounsfield threshold and morphological constraints. The combined effect of limited spatial resolution and motion blur in the SPN's PET image was then modelled by an effective Gaussian point-spread function (psf). Both isotropic and non-isotropic psfs were used. To validate the method, PET/CT measurements of the NEMA/IEC spheres phantom were performed. The method was applied to 50 unselected SPNs <or=30 mm from routine patient care. Recovery of standardised uptake value (SUV) in the phantom image was significantly improved by combined recovery and motion blur correction compared with recovery-only correction, particularly with the non-isotropic model (residual average error 10%). In the patient images, automated segmentation and fit of the effective psf worked properly in all cases. Volume-equivalent diameter ranged from 4.9 to 27.8 mm. Uncorrected maximum SUV ranged from 0.9 to 13.3. Compared with recovery-only correction, combined correction with the non-isotropic model resulted in a 'relevant' (>or=30%) SUV increase in 47 SPNs (94%). Correction of both recovery and motion blur is mandatory for accurate SUV quantification of SPNs.

Motion correction of PET brain images through deconvolution: I. Theoretical development and analysis in software simulations

Image quality is significantly degraded even by small amounts of patient motion in very high-resolution PET scanners. Existing correction methods that use known patient motion obtained from tracking devices either require multi-frame acquisitions, detailed knowledge of the scanner, or specialized reconstruction algorithms. A deconvolution algorithm has been developed that alleviates these drawbacks by using the reconstructed image to estimate the original non-blurred image using maximum likelihood estimation maximization (MLEM) techniques. A high-resolution digital phantom was created by shape-based interpolation of the digital Hoffman brain phantom. Three different sets of 20 movements were applied to the phantom. For each frame of the motion, sinograms with attenuation and three levels of noise were simulated and then reconstructed using filtered backprojection. The average of the 20 frames was considered the motion blurred image, which was restored with the deconvolution algorithm. After correction, contrast increased from a mean of 2.0, 1.8 and 1.4 in the motion blurred images, for the three increasing amounts of movement, to a mean of 2.5, 2.4 and 2.2. Mean error was reduced by an average of 55% with motion correction. In conclusion, deconvolution can be used for correction of motion blur when subject motion is known.

Combined motion blur and partial volume correction for computer aided diagnosis of pulmonary nodules in PET/CT

Objective We present an automated scheme to correct PET max-uptake-values of small to medium-sized pulmonary nodules for motion blur and partial volume averaging. Both effects cause significant underestimation of PET max-uptake-values, particularly in nodules below 25 mm diameter, but nodules up to 75 mm might be affected. This compromises the power of PET for the differential diagnosis of such nodules, in particular benign versus malignant. Thus, correcting PET max-uptake-values has the potential to improve the classification of PET-positive pulmonary nodules.