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.

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.

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.

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.

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.

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.

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.

Single image super-resolution via blind blurring estimation and anchored space mapping

It has been widely acknowledged that learning-based super-resolution(SR) methods are effective to recover a high resolution(HR) image from a single low resolution(LR) input image. However,there exist two main challenges in learning-based SR methods currently: the quality of training samples and the demand for computation. We proposed a novel framework for single image SR tasks aiming at these issues, which consists of blind blurring kernel estimation(BKE) and SR recovery with anchored space mapping(ASM). BKE is realized via minimizing the cross-scale dissimilarity of the image iteratively, and SR recovery with ASM is performed based on iterative least square dictionary learning algorithm(ILS-DLA). BKE is capable of improving the compatibility of training samples and testing samples effectively and ASM can reduce consumed time during SR recovery radically.Moreover, a selective patch processing(SPP) strategy measured by average gradient amplitude |grad| of a patch is adopted to accelerate the BKE process. The experimental results show that our method outruns several typical blind and non-blind algorithms on equal conditions.

Defocus Hyperspectral Image Deblurring with Adaptive Reference Image and Scale Map

Defocus blur is one of the primary problems among hyperspectral imaging systems equipped with simple lenses. Most of the previous deblurring methods focus on how to utilize structure information of a single channel, while ignoring the characteristics of hyperspectral images. In this work, we analyze the correlations and differences among spectral channels, and propose a deblurring framework for defocus hyperspectral images. First, we divide the hyperspectral image channels into two sets, and the set with less blur is treated as a group of spectral bases. Then, according to the inherent correlations of spectral channels, a reference image can be derived from the spectral bases to guide the restoration of blurry channels. Finally, considering the disagreement between the reference image and the ground truth, a scale map based on gradient similarity is introduced as a prior in the deblurring framework. The experimental results on public dataset demonstrate that the proposed method outperforms several image deblurring methods in both visual effect and quality metrics.

Real-time motion deblurring algorithm with robust noise suppression

In an image restoration process,to obtain good results is challenging because of the unavoidable existence of noise even if the blurring information is already known.To suppress the deterioration caused by noise during the image deblurring process,we propose a new deblurring method with a known kernel.First,the noise in the measurement process is assumed to meet the Gaussian distribution to fit the natural noise distribution.Second,the first and second orders of derivatives are supposed to satisfy the independent Gaussian distribution to control the non-uniform noise.Experimental results show that our method is obviously superior to the Wiener filter,regularized filter,and Richardson-Lucy(RL) algorithm.Moreover,owing to processing in the frequency domain,it runs faster than the other algorithms,in particular about six times faster than the RL algorithm.

Multiple Data Augmentation Strategy for Enhancing the Performance of YOLOv7 Object Detection Algorithm

The object detection technique depends on various methods for duplicating the dataset without adding more images.Data augmentation is a popularmethod that assists deep neural networks in achieving better generalization performance and can be seen as a type of implicit regularization.Thismethod is recommended in the casewhere the amount of high-quality data is limited,and gaining new examples is costly and time-consuming.In this paper,we trained YOLOv7 with a dataset that is part of the Open Images dataset that has 8,600 images with four classes(Car,Bus,Motorcycle,and Person).We used five different data augmentations techniques for duplicates and improvement of our dataset.The performance of the object detection algorithm was compared when using the proposed augmented dataset with a combination of two and three types of data augmentation with the result of the original data.The evaluation result for the augmented data gives a promising result for every object,and every kind of data augmentation gives a different improvement.The mAP@.5 of all classes was 76%,and F1-score was 74%.The proposed method increased the mAP@.5 value by+13%and F1-score by+10%for all objects.

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.

基于网页的CSS滤镜

CSS滤镜能把可视化的滤镜和转换效果添加到一个标准的 HTML元素上 ,可以根据需要作用于多个页面 ,甚至整个站点 .滤镜是通过“filter”样式表单属性来对 HTML产生作用的 .本文论述了 CSS滤镜的主要属性 。

用投影迭代法实现匀速直线运动模糊图像的复原

1 投影迭代公式在线性空不变条件下的简化图像复原的算法很多,本文采用Huang提出的投影迭代复原算法,它适用于线性模糊图像的复原.对线性成像系统,模糊图像g(x, y)与对应的原始图像f(x,y)的离散数字化关系为g=Df,其中g,f分别是g(x,y),f(x,y)的向量表示,D是由系统点扩展函数决定的转换矩阵。

Design of a Tree Ring Structure Analysis System to Estimate the Accurate Age of Tree Species in Sri Lanka

Determination of an age in a particular tree species can be considered as a vital factor in forest management.In this research we have introduced a novel scheme to determine the accurate age of the tree species in Sri Lanka.This is initially developed for the tree species called‘Hora’(Dipterocarpus zeylanicus)in wet zone of Sri Lanka.Here the core samples are extracted and further analyzed by means of the different image processing techniques such as Gaussian kernel blurring,use of Sobel filters,double threshold analysis,Hough line tran sformation and etc.The operations such as rescaling,slicing and measuring are also used in line with image processing techniques to achieve the desired results.Ultimately a Graphical user interface(GUI)is developed to cater for the user requirements in a user friendly environment.It has been found that the average growth ring identification accuracy of the proposed system is 93%and the overall average accuracy of detecting the age is 81%.Ultimately the proposed system will provide an insight and contributes to the forestry related activities and researches in Sri Lanka.

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.

Characteristics of symptoms of imminent eclampsia: A case referent study from a tertiary hospital in Tanzania

Background: Maternal mortality in developing countries is unacceptably high with eclampsia being consistently among the top causes. As yet, primary prevention of this complication is not possible since causes of preeclampsia are largely unknown and bio-chemical, hematological and radiological markers have proved unsuitable for routine prediction of eclamptic fits. Although headache, visual disturbance, abdominal pain, nausea, and vomiting are routinely elicited when managing pre-eclampsia and have been reported to predict eclamptic fits, the literature attempting to characterize them is scanty. We sought to establish characteristics of the prodromal symptoms of eclampsia and compare them with similar symptoms as experienced by normotensive pregnant women at Muhimbili National Hospital (MNH) in Tanzania. Methods: This study was conducted at MNH in 2010 by enrolling 123 eclamptic and 123 normotensive women. Women in the two groups were interviewed about their experiences and characteristics of headache, visual disturbances, abdominal pain, nausea and vomiting using a semi structured questionnaire. The severity, nature and other characteristics of the symptoms were assessed using standard scale/methods and data compared among the two groups. Results: Prodromal symptoms of eclampsia were present in 90% of eclamptic women. Headache was more frequent among eclamptic women (88%) than the normotensive (43%), p < 0.001). The symptom was also more perceived as severe among eclamptic (46.3%) than the normotensive (5.7%), p < 0.001. The most frequent location for headache was frontal in 65.7% of eclamptic women compared to frontal (41.5%) or generalized (39.6%) for the normotensive. Likewise, visual problems were significantly more frequent among eclamptic women (39%) compared to the normotensive (3%), p < 0.001. Upper abdominal pain was significantly more reported by eclamptic (36%) than normotensive women (0.9%), p = 0.001. The general occurrence of abdominal pain, nausea and vomiting was not significantly different in the two groups. The time lag from development of a symptom to eclamptic fit was up to seven days for most symptoms except visual disturbances of which 98% developed fits within 12 hours. Conclusion: Whereas the prodromal symptoms of eclampsia and similar symptoms in normotensive women were common, the characteristics of headache and visual disturbance differ significantly in the two groups. The knowledge of these differences could be utilized to improve the quality of management of pre eclamptic women in order to prevent eclampsia.

基于OpenCL的图像模糊化算法优化研究

现代GPU一般都提供特定硬件(如纹理部件、光栅化部件及各种片上缓存)以加速二维图像的处理和显示过程,相应的编程模型(CUDA、OpenCL)都定义了特定程序设计接口(CUDA的纹理内存,OpenCL的图像对象)以便图像应用能利用相关硬件支持。以典型图像模糊化处理算法在AMD平台GPU的优化为例,探讨了OpenCL的图像对象在图像算法优化上的适用范围,尤其是分析了其相对于更通用的基于全局内存加片上局部存储进行性能优化的方法的优劣。实验结果表明,图像对象只有在图像为四通道且计算过程中需要缓存的数据量较小时才能带来较好的性能改善,其余情况采用全局内存加局部存储都能获得较好性能。优化后的算法性能相对于精心实现的CPU版加速比为200～1000;相对于NVIDIA NPP库相应函数的性能加速比为1.3～5。

A Local Binary Pattern-Based Method for Color and Multicomponent Texture Analysis

Local Binary Patterns (LBPs) have been highly used in texture classification for their robustness, their ease of implementation and their low computational cost. Initially designed to deal with gray level images, several methods based on them in the literature have been proposed for images having more than one spectral band. To achieve it, whether assumption using color information or combining spectral band two by two was done. Those methods use micro structures as texture features. In this paper, our goal was to design texture features which are relevant to color and multicomponent texture analysis without any assumption. Based on methods designed for gray scale images, we find the combination of micro and macro structures efficient for multispectral texture analysis. The experimentations were carried out on color images from Outex databases and multicomponent images from red blood cells captured using a multispectral microscope equipped with 13 LEDs ranging from 375 nm to 940 nm. In all achieved experimentations, our proposal presents the best classification scores compared to common multicomponent LBP methods. 99.81%, 100.00%, 99.07% and 97.67% are maximum scores obtained with our strategy respectively applied to images subject to rotation, blur, illumination variation and the multicomponent ones.

用AE打造逼真的“文字流星雨”特效

电视片头和广告中经常看到出现下落的流星雨效果,它能够起到活跃气氛、加强节奏的作用。在此,我们用AE的Particle Playground制作这种效果,操作简单,无须任何插件。