A progressive framework for rotary motion deblurring

The rotary motion deblurring is an inevitable procedure when the imaging seeker is mounted in the rotating missiles.Traditional rotary motion deblurring methods suffer from ringing artifacts and noise,especially for large blur extents.To solve the above problems,we propose a progressive rotary motion deblurring framework consisting of a coarse deblurring stage and a refinement stage.In the first stage,we design an adaptive blur extents factor(BE factor)to balance noise suppression and details reconstruction.And a novel deconvolution model is proposed based on BE factor.In the second stage,a triplescale deformable module CNN(TDM-CNN)is designed to reduce the ringing artifacts,which can exploit the 2D information of an image and adaptively adjust spatial sampling locations.To establish a standard evaluation benchmark,a real-world rotary motion blur dataset is proposed and released,which includes rotary blurred images and corresponding ground truth images with different blur angles.Experimental results demonstrate that the proposed method outperforms the state-of-the-art models on synthetic and real-world rotary motion blur datasets.The code and dataset are available at https://github.com/JinhuiQin/RotaryDeblurring.

No-Reference Blur Assessment Based on Re-Blurring Using Markov Basis

Blur is produced in a digital image due to low passfiltering,moving objects or defocus of the camera lens during capture.Image viewers are annoyed by blur artefact and the image's perceived quality suffers as a result.The high-quality input is relevant to communication service providers and imaging product makers because it may help them improve their processes.Human-based blur assessment is time-consuming,expensive and must adhere to subjective evaluation standards.This paper presents a revolutionary no-reference blur assessment algorithm based on reblurring blurred images using a special mask developed with a Markov basis and Laplacefilter.Thefinal blur score of blurred images has been calculated from the local variation in horizontal and vertical pixel intensity of blurred and re-blurred images.The objective scores are generated by applying proposed algorithm on the two image databases i.e.,Laboratory for image and video engineering(LIVE)database and Tampere image database(TID 2013).Finally,on the basis of objective and subjective scores performance analysis is done in terms of Pearson linear correlation coefficient(PLCC),Spearman rank-order correlation coefficient(SROCC),Mean absolute error(MAE),Root mean square error(RMSE)and Outliers ratio(OR).The existing no-reference blur assessment algorithms have been used various methods for the evaluation of blur from no-reference image such as Just noticeable blur(JNB),Cumulative Probability Distribution of Blur Detection(CPBD)and Edge Model based Blur Metric(EMBM).The results illustrate that the proposed method was successful in predicting high blur scores with high accuracy as compared to existing no-reference blur assessment algorithms such as JNB,CPBD and EMBM algorithms.

Image defocus deblurring method based on gradient difference of boundary neighborhood

Background For static scenes with multiple depth layers,existing defocused image deblurring methods have the problems of edge-ringing artifacts or insufficient deblurring owing to inaccurate estimation of the blur amount,and prior knowledge in nonblind deconvolution is not strong,which leads to image detail recovery challenges.Methods To this end,this study proposes a blur map estimation method for defocused images based on the gradient difference of the boundary neighborhood,which uses the gradient difference of the boundary neighborhood to accurately obtain the amount of blurring,thereby preventing boundary ringing artifacts.The obtained blur map is then used for blur detection to determine whether the image needs to be deblurred,thereby improving the efficiency of deblurring without manual intervention and judgment.Finally,a nonblind deconvolution algorithm was designed to achieve image deblurring based on the blur amount selection strategy and sparse prior.Results Experimental results showed that our method improves PSNR(Peak Signal-to-Noise Ratio)and SSIM(Structural Similarity Index)by an average of 4.6%and 7.3%,respectively,compared to existing methods.Conclusions Experimental results showed that the proposed method outperforms existing methods.Compared to existing methods,our method can better solve the problems of boundary ringing artifacts and detail information preservation in defocused image deblurring.

Camera Independent Motion Deblurring in Videos Using Machine Learning

In this paper, we will be looking at our efforts to find a novel solution for motion deblurring in videos. In addition, our solution has the requirement of being camera-independent. This means that the solution is fully implemented in software and is not aware of any of the characteristics of the camera. We found a solution by implementing a Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) hybrid model. Our CNN-LSTM is able to deblur video without any knowledge of the camera hardware. This allows it to be implemented on any system that allows the camera to be swapped out with any camera model with any physical characteristics.

Application of Image Compression to Multiple-Shot Pictures Using Similarity Norms With Three Level Blurring

be stored or transmitted in an efficient form.In this work,a new idea is proposed,where we take advantage of the redundancy that appears in a group of images to be all compressed together,instead of compressing each image by itself.In our proposed technique,a classification process is applied,where the set of the input images are classified into groups based on existing technique like L1 and L2 norms,color histograms.All images that belong to the same group are compressed based on dividing the images of the same group into sub-images of equal sizes and saving the references into a codebook.In the process of extracting the different sub-images,we used the mean squared error for comparison and three blurring methods(simple,middle and majority blurring)to increase the compression ratio.Experiments show that varying blurring values,as well as MSE thresholds,enhanced the compression results in a group of images compared to JPEG and PNG compressors.

Restoration of space-variant blurred image based on motion-blurred target segmentation

In imaging on moving target, it is easy to get space- variant blurred image. In order to recover the image and gain recognizable target, an approach to recover the space-variant blurred image is presented based on image segmentation. Be- cause of motion blur＇s convolution process, the pixels of observed image＇s target and background will be displaced and piled up to produce two superposition regions. As a result, the neighbor- ing pixels in the superposition regions will have similar grey level change. According to the pixel＇s motion-blur character, the target＇s blurred edge of superposition region could be detected. Canny operator can be recurred to detect the target edge which parallels the motion blur direction. Then in the segmentation process, the whole target image which has the character of integral convolution between motion blur and real target image can be obtained. At last, the target image is restored by deconvolution algorithms with adding zeros. The restoration result indicates that the approach can effectively solve the kind of problem of space-variant motion blurred image restoration.

BaMBNet:A Blur-Aware Multi-Branch Network for Dual-Pixel Defocus Deblurring

Reducing the defocus blur that arises from the finite aperture size and short exposure time is an essential problem in computational photography.It is very challenging because the blur kernel is spatially varying and difficult to estimate by traditional methods.Due to its great breakthrough in low-level tasks,convolutional neural networks(CNNs)have been introdu-ced to the defocus deblurring problem and achieved significant progress.However,previous methods apply the same learned kernel for different regions of the defocus blurred images,thus it is difficult to handle nonuniform blurred images.To this end,this study designs a novel blur-aware multi-branch network(Ba-MBNet),in which different regions are treated differentially.In particular,we estimate the blur amounts of different regions by the internal geometric constraint of the dual-pixel(DP)data,which measures the defocus disparity between the left and right views.Based on the assumption that different image regions with different blur amounts have different deblurring difficulties,we leverage different networks with different capacities to treat different image regions.Moreover,we introduce a meta-learning defocus mask generation algorithm to assign each pixel to a proper branch.In this way,we can expect to maintain the information of the clear regions well while recovering the missing details of the blurred regions.Both quantitative and qualitative experiments demonstrate that our BaMBNet outperforms the state-of-the-art(SOTA)methods.For the dual-pixel defocus deblurring(DPD)-blur dataset,the proposed BaMBNet achieves 1.20 dB gain over the previous SOTA method in term of peak signal-to-noise ratio(PSNR)and reduces learnable parameters by 85%.The details of the code and dataset are available at https://github.com/junjun-jiang/BaMBNet.

Defocus Blur Segmentation Using Genetic Programming and Adaptive Threshold

Detection and classification of the blurred and the non-blurred regions in images is a challenging task due to the limited available information about blur type,scenarios and level of blurriness.In this paper,we propose an effective method for blur detection and segmentation based on transfer learning concept.The proposed method consists of two separate steps.In the first step,genetic programming(GP)model is developed that quantify the amount of blur for each pixel in the image.The GP model method uses the multiresolution features of the image and it provides an improved blur map.In the second phase,the blur map is segmented into blurred and non-blurred regions by using an adaptive threshold.A model based on support vector machine(SVM)is developed to compute adaptive threshold for the input blur map.The performance of the proposed method is evaluated using two different datasets and compared with various state-of-the-art methods.The comparative analysis reveals that the proposed method performs better against the state-of-the-art techniques.

Defocus Blur Segmentation Using Local Binary Patterns with Adaptive Threshold

Enormousmethods have been proposed for the detection and segmentation of blur and non-blur regions of the images.Due to the limited available information about blur type,scenario and the level of blurriness,detection and segmentation is a challenging task.Hence,the performance of the blur measure operator is an essential factor and needs improvement to attain perfection.In this paper,we propose an effective blur measure based on local binary pattern(LBP)with adaptive threshold for blur detection.The sharpness metric developed based on LBP used a fixed threshold irrespective of the type and level of blur,that may not be suitable for images with variations in imaging conditions,blur amount and type.Contrarily,the proposed measure uses an adaptive threshold for each input image based on the image and blur properties to generate improved sharpness metric.The adaptive threshold is computed based on the model learned through support vector machine(SVM).The performance of the proposed method is evaluated using two different datasets and is compared with five state-of-the-art methods.Comparative analysis reveals that the proposed method performs significantly better qualitatively and quantitatively against all of the compared methods.

粒子系统与Blur火焰算法的性能比较

火焰算法是计算机图形学中的一个热点,目前已经出现了多种不同的实现算法.本文采用Java语言实现了基于粒子系统的算法和Blur算法,并对两种算法的空间复杂度、时间复杂度和视觉效果进行了分析与比较.

Quantification of Blur and Its Applications

Human visual sense has two aspects in our feeling for blurred image, that is, one is the amount of blur depending on object size, the other is the amount of blur independent of the object size. In the former for example, when the image size becomes larger, the author feels smaller amount blur. The quantitative evaluation based on entropy for blurred images is proposed in this paper. The author calls this metric ＂variation entropy＂. This metric has two kinds of aspects that coincide with the human visual sense. The first is the absolute evaluation of blur, and the second is the relative evaluation of blur. The former can be quantified by variation entropy for a unit boundary length （or L-type variation entropy： HL ）, which is dependent on resolution, and the latter can be quantified by variation entropy for a unit area （or A-type variation entropy： H^A ）, which is independent of resolution. These two metrics have complementary properties. At last, two variation entropies are applied to the standard kanji character database, and then the strong relation between variation entropy and accuracy of recognition is discussed. The tendency of writing skills for grades is evaluated by applying the metric to a database collected from school children.

AB102.Image blur perception in amblyopia:beyond edges

Background:Understanding the neurophysiological mechanisms of Amblyopia,a neurodevelopmental disorder of the visual cortex,will bring us closer to full recovery.Past findings have been contradictory.Results have shown that despite having severe acuity impairment,amblyopes can nonetheless perceive sharp edges.In this study,we explore the representation of blur through a series of image blur-discrimination and matching tasks,to understand more about the amblyopes’visual system.Methods:Monocular image blur-discrimination thresholds were measured in a spatial two-alternative forced-choice procedure whereby subjects had to decide which image was the blurriest.Subjects also had to interocularly match pictures that were identical to those used for the image blur discrimination task.Ten amblyopes,as well as a group of ten controls were under study.Results:Data on amblyopes and controls will be presented for both experiments.According to previous research that was done on blur-edge discrimination and matching,we predict that subjects’performance will follow a dipper function,that is,all observers will be better at discriminating between both images when a small amount of blur is applied rather than when the image is either sharp or very blurry.We also predict that amblyopes’blur discrimination will be noisier,but that they will paradoxically be able to match the sharpness of the images presented in the matching task.Conclusions:This would confirm our hypothesis about amblyopes’visual system,that they can represent blur levels defined by spatial frequencies that are beyond their resolution limit,and would also raise interesting questions about the visual system in general regarding the different perceptions driven by images versus edges.

Blind Motion Deblurring for Online Defect Visual Inspection

Online defect visual inspection (ODVI) works while the object has to be static, otherwise the relative motion between camera and object will create motion blur in images. In order to implement ODVI in dynamic scene, it developes one blind motion deblurring method whose objective is to estimate blur kernel parameters precisely. In the proposed method, Radon transform on superpixels determinated the blur angle, and the autocorrelation function based on magnitude (AFM) of the preprocessed blurred image was utilized to identify the blur length. With the projection relationship discussed in this study, it will be unnecessary to rotate the blurred image or the axis. The proposed method is of high accuracy and robustness to noise, and it can somehow handle saturated pixels. To validate the proposed method, experiments have been carried out on synthetic images both in noise free and noisy situations. The results show that the method outperforms existing approaches. With the modified Richardson– Lucy deconvolution, it demonstrates that the proposed method is effective for ODVI in terms of subjective visual quality.

Parameter Estimation for Blur Image Combining Defocus and Motion Blur using Cepstrum Analysis

The degraded parameters recognition is very important for the restoration of blurred images. There are two common types of blurs for most camera systems. One is the defocus blur due to the optical system's defocus phenomenon and the other is the motion blur due to the relative movement between the objectives and the camera. Compared with the recognition for the blurred image with only one blur model, the parameter estimation for the picture combining defocus and motion blur models is a more complicated mission. A method was proposed for computer to estimate the parameters of defocus blur and motion blur in cepstrum area simultaneously. According to characters of both blur models in the frequency domain, an adjustment approach was suggested in the frequency area and then convert to the cepstrum field to increase the accuracy of measurement.

3D Reconstruction for Motion Blurred Images Using Deep Learning-Based Intelligent Systems

The 3D reconstruction using deep learning-based intelligent systems can provide great help for measuring an individual’s height and shape quickly and accurately through 2D motion-blurred images.Generally,during the acquisition of images in real-time,motion blur,caused by camera shaking or human motion,appears.Deep learning-based intelligent control applied in vision can help us solve the problem.To this end,we propose a 3D reconstruction method for motion-blurred images using deep learning.First,we develop a BF-WGAN algorithm that combines the bilateral filtering(BF)denoising theory with a Wasserstein generative adversarial network(WGAN)to remove motion blur.The bilateral filter denoising algorithm is used to remove the noise and to retain the details of the blurred image.Then,the blurred image and the corresponding sharp image are input into the WGAN.This algorithm distinguishes the motion-blurred image from the corresponding sharp image according to the WGAN loss and perceptual loss functions.Next,we use the deblurred images generated by the BFWGAN algorithm for 3D reconstruction.We propose a threshold optimization random sample consensus(TO-RANSAC)algorithm that can remove the wrong relationship between two views in the 3D reconstructed model relatively accurately.Compared with the traditional RANSAC algorithm,the TO-RANSAC algorithm can adjust the threshold adaptively,which improves the accuracy of the 3D reconstruction results.The experimental results show that our BF-WGAN algorithm has a better deblurring effect and higher efficiency than do other representative algorithms.In addition,the TO-RANSAC algorithm yields a calculation accuracy considerably higher than that of the traditional RANSAC algorithm.

Blind-restoration-based blind separation method for permuted motion blurred images

A novel single-channel blind separation algorithm for permuted motion blurred images is proposed by using blind restoration in this paper. Both the motion direction and the length of the point spread function （PSF） are estimated by Radon transformation and extrema a detection. Using the estimated blur parameters, the permuted image is restored by performing the L-R blind restoration method. The permutation mixing matrices can be accurately estimated by classifying the ringing effect in the restored image, thereby the source images can be separated. Simulation results show a better separation efficiency for the permuted motion blurred image with various permutation operations. The proposed algorithm indicates a better performance on the robustness against Gaussian noise and lossy JPEG compression.

Numericals for total variation-based reconstruction of motion blurred images

In this paper image with horizontal motion blur, vertical motion blur and angled motion blur are considered. We construct several difference schemes to the highly nonlinear term △↓.（△↓u/√｜△↓｜^2＋β） of the total variation-based image motion deblurring problem. The large nonlinear system is linearized by fixed point iteration method. An algebraic multigrid method with Krylov subspace acceleration is used to solve the corresponding linear equations as in [7]. The algorithms can restore the image very well. We give some numerical experiments to demonstrate that our difference schemes are efficient and robust.

Parameter recognition for defocus blur image using cepstrum analysis

Successful restoration of blurred images depends primarily on the knowledge about the degradationparameter.Defocus blur model in the frequency domain is characterized by concentric rings and the blurradius of the point spread function(PSF)can be identified conveniently in the frequency field for peopleby manual means rather than for computer.This paper introduces a practical method for computer to esti-mate the defocus blur parameter in cepstrum area.Fourier transform plays an intermediate role in the pathto cepstrum domain.We suggest a weighted adjustment operation in the frequency domain and then con-vert it to the cepstrum field to increase the accuracy of recognition.

MEASUREMENT OF ANGULAR VIBRATION AMPLITUDE BY ACTIVELY BLURRED IMAGES

A novel motion-blur-based method for measuring the angular amplitude of a high-frequency rotational vibration is schemed. The proposed approach combines the active vision concept and the mechanism of motion-from-blur, generates motion blur on the image plane actively by extending exposure time, and utilizes the motion blur information in polar images to estimate the angular amplitude of a high-frequency rotational vibration. This method obtains the analytical results of the angular vibration amplitude from the geometric moments of a motion blurred polar image and an unblurred image for reference. Experimental results are provided to validate the presented scheme.

Influence of Blurred Ways on Pattern Recognition of a Scale-Free Hopfield Neural Network

We investigate the influence of blurred ways on pattern recognition of a Barabasi-Albert scale-free Hopfield neural network （SFHN） with a small amount of errors. Pattern recognition is an important function of information processing in brain. Due to heterogeneous degree of scale-free network, different blurred ways have different influences on pattern recognition with same errors. Simulation shows that among partial recognition, the larger loading ratio （the number of patterns to average degree P/ （k） ） is, the smaller the overlap of SFHN is. The influence of directed （large） way is largest and the directed （small） way is smallest while random way is intermediate between them. Under the ratio of the numbers of stored patterns to the size of the network PIN is less than O. 1 conditions, there are three families curves of the overlap corresponding to directed （small）, random and directed （large） blurred ways of patterns and these curves are not associated with the size of network and the number of patterns. This phenomenon only occurs in the SFHN. These conclusions are benefit for understanding the relation between neural network structure and brain function.