Blurriness is one of the common distortions in images. This distortion is caused by spilling the pixel information overthe adjacent pixels. Blurriness has different types. The knowledge about the type of image blurriness is one of the important parameters which directly affects performance of de-blurring methods. In this paper, a method has been proposed to classify the fourtypes of global blurrinessin digital imagesin the spatial domain. These blurriness include the Gaussian blur, rectangular blur, motion blur and defocus blur. In the proposed method, the correlation concept is used to classify the type of image blurriness. The correlation concept depicts the relations between the image pixels. Also, the model and correlation of adjacent pixels are proportional to the type of blurriness. Appropriate features are extracted to detect the type of blurriness. The accuracy of the proposed method for detecting the type of blurriness is 90. 4%. This method has a better performance compared to other existing methods in terms of accuracy and computational cost.

Flexible ropes have wide-ranging applications in aerospace engineering, yet accurately measuring their motion state without disrupting dynamic characteristics remains a challenge. This study introduces a visual measurement method aimed at precisely assessing flexible rope motion to support the development and validation of an accurate cable dynamics model. Addressing non-uniform movement speeds attributed to the rope's large length-diameter ratio, a novel tether edge segmentation operator is proposed to delineate motion blur regions into exposure beginning and ending time tethers. This operator enhances accuracy over existing centerline extraction methods, particularly in asymmetric motion blur regions. The proposed approach not only resolves accuracy issues during high-speed motion but also leverages the camera's inherent image acquisition frame rate, reducing system complexity and cost. Validation of the material point tracking algorithm through mathematical and physical simulations underscores its effectiveness in monitoring any point on the tether. Furthermore, verifying the tether dynamics model through the absolute nodal coordinate method highlights the novelty and significance of this research in advancing aerospace engineering applications.

Study of ground motion attenuation in Tehran region is a very important aspect of determining a more precise hazard map of the city. For the last 10 years, three short period seismic networks have been operating in the study region by the Institute of Geophysics, University of Tehran (IGUT). We have selected 47 events recorded by IGUT stations during 1996-2004 to estimate attenuation parameters for the study area. The selected events have provided 480 records with good spatial resolution. Only records with signal-to-noise ratio of greater than 4 have been selected. To find the distances at which the nature of geometrical spreading attenuation (R-b) changes significantly, we use a local regression smoothing method called Robust Lowess. It is found that a tri-linear function having hinges at distances about 106±10 and 191±10 km describes the geometric spreading attenuation with distance. Using a tri-linear regression analysis, we found that b1=1.1±0.1, b2=-0.4±0.1, b3=0.5 minimize the average absolute value of the residuals at a frequency of 4 Hz. The remaining attenuation is assumed to be caused by anelasticity. Using anelastic attenuation at different frequencies, the quality factor in Tehran region is obtained as Q=121±3ƒ0.68±0.02.

Estimation of the blurriness value in an image is an important issue in the image processing applications such as image deblurring. In this paper, a no-reference blur metric with a low computational cost is proposed, which is based on the difference between the second-order gradients of a sharp image and the one associated with its blurred version. The experiments, in this work, are performed on four databases including CSIQ, TID2008, IVC, and LIVE. The experimental results obtained indicate the capability of the proposed blur metric in measuring image blurriness and also the low computational cost compared with the other existing approaches.

Earthquake wave transmission from the source to the site is modeled by the ground motion prediction equations (GMPEs or attenuation equations). Considering the high contribution of these equations in the variability of the ground motion in the site, the selection of the appropriate relations for the region is of particular importance. The number of prediction equations is large, but there is no specific relationship for many areas, so it is necessary to use criteria to determine the best equations for probabilistic seismic hazard analysis(PSHA). In this paper, 62 relationships used by analysts in Iran and Tehran region are introduced to the three criteria of Rennie divergence, Euclidean distance and likelihood method, are ranked based on similarity with actual occurrence data, and the most suitable relationships for Tehran are introduced. The results show that the relationship of Zare and Sabze Ali (2006) in all three methods has a very good match with the Tehran region earthquakes. Also, some global relations have a better match than the regional relations obtained in Tehran. The results of the sensitivity analysis show a decrease in the effectiveness of the regional PSHA using the ranking. It also shows that relying on the opinion of experts brings more than 90% confidence in the selection of GMPEs, which seems sufficient in some cases. .If this level of accuracy is not sufficient, it is necessary to use the appropriate weighted equations in the analysis based on the physics-based ranking results. Otherwise, the results of the probabilistic seismic hazard analysis will fluctuate and may not have the sufficient reliability.

Un-sharp masking method improves the images contrast without requiring any prior knowledge. In this method, a sharper image can be achieved by empowering the high frequency components of the input image. Un-sharp masking has a parameter named gain factor which has a high effect on the enhanced image quality. In this paper, an approach is proposed to adaptively estimate the appropriate value of this parameter in order to effectively enhance an image with local blur, or an image with non-uniform blur. In proposed method, first, the input image is segmented into blur and non-blur regions. Then the gain factor is estimated for each region adaptively. In this approach, the influence of the image blurriness on its gradient information is used to estimate the value for the gain factor. The image quality assessments are applied to evaluate the performance of proposed un-sharp masking method in image enhancement. Experimental results demonstrate that the performance of our proposed method is better than the performance of existing un-sharp masking methods in image enhancement.

Blur is one of the common image distortions in which fraction of a pixel value، depending on the severity of blur، is added to one or moreneighboring pixels. Knowledge about the severity of blurrinessin a given image is very importantfor image enhancementpurposes. Sinceblur severity is not generally constant at all pixels، it is necessary to accurately estimate the blurriness value of pixels in the image (the so called blur map). In this paper، a methodis proposed for this purpose. The proposed method in this paper provides the blur map via estimating the blurriness of the pixels based on the difference between surrounding block of the pixel and its blurred version. Our investigations indicate that، in general، the blur image regions have lower frequency content than the un-blur (sharp) regions. The results of extensive experiments on a large database including motion and defocus blurred images confirmthe superiority of the proposed approachover the existing methods.

Purpose: Ensuring excellent video quality is crucial for the success of minimally invasive surgical procedures without disrupting the surgical procedure flow. Real-time laparoscopic video frequently encounters issues such as blur and smoke, often stemming from lens contamination. The automatic detection of these distortions is imperative to assist surgeons, ultimately reducing operative time and mitigating risks for the patient. Materials and Methods: In this paper, we leverage the Laparoscopic Video Quality (LVQ) database developed by Khan et al. to train and validate our model. To classify defocus blur, motion blur, and smoke in the laparoscopic video, we adopt a novel approach utilizing a cascade support vector machine (SVM) classifier, which combines decisions from three binary classifiers. The first classifier categorizes videos into two classes: good and distorted. The second classifier focuses on detecting smoke and blur, while the third is dedicated to distinguishing between defocus blur and motion blur. Results: In this study, we calculate performance metrics, including accuracy rate, precision, recall, F1 score, and execution time, which are crucial indicators for evaluating quality detection results. The machine-learning classification demonstrates notable performance, with an accuracy rate of 96.55% for the first classifier, 100% for the second, and 99.67% for the third classifier. Additionally, the classification achieves a high inference speed of 37 frames per second (fps). Conclusion: The experimental results showcased in this paper underscore the efficacy of the proposed approach in automatically detecting distortions in a laparoscopic video. The method exhibits high performance, excelling in both accuracy and processing speed. Notably, the method's advantage lies in its simplicity and the fact that it does not necessitate high-performance computer hardware.

Super-resolution (SR) is a technique that produces a high resolution (HR) image via employing a number of low resolution (LR) images from the same scene. One of the degradations that attenuates performance of the SR is the blurriness of the input LR images. In many previous works in the SR, the blurriness of the LR images is assumed to be due to the integral effect of the image sensor of the acquisition device. However, in practice there are some other factors that blur the LR images, such as diffraction, motion of the object and/or acquisition device, atmospheric blurring and defocus blurring. To apply the super-resolution process accurately, the degradation model applied to HR image leading to LR ones needs to be known. In this paper, we aim to use the LR images blurriness to find the blurring kernel applied on the HR image. Hence, we setup a simulation experiment in which the blurring kernel is limited to be one of the predetermined kernels. In the experiment, the blurriness of the LR images is supposed to be unknown, and is estimated using a blur kernel estimation method. Then, the estimated blur kernels of the LR images are fed to an artificial neural network (ANN) to determine the blur kernels associated with the HR image. Experiment results show the use of determined blur kernels improves the quality of output HR image.