Relevant bibliographies by topics / Image Blur Detection

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Image Blur Detection'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 February 2022

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Journal articles on the topic "Image Blur Detection"

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Wei, Li Xian, Jun Jie Zhu, and Xiao Yuan Yang. "An Image Forensics Algorithm for Blur Detection Based on Properties of Sharp Edge Points ." Advanced Materials Research 341-342 (September 2011): 743–47. http://dx.doi.org/10.4028/www.scientific.net/amr.341-342.743.

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This paper proposes a algorithm for detecting manual blur on images, which is usually used to remove obvious traces when tamper images. The algorithm first blurs the test image and blocks the both test image and blurred image. Then extracts and compares the sharp edge points in contourlet domain of the two images, so as to detect the suspicious blurred blocks. Furthermore, differences between manual blur and defocus blur can be indicated by our proposed method, and we can find out whether the image has been manual blurred. We establish a rich set of experimental images, and test results show that the average accurate detection rate is high, and the tampered regions can be always located. Our next work is to improve the robustness of the algorithm.
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Teo, Tee-Ann, and Kai-Zhi Zhan. "INTEGRATION OF IMAGE-DERIVED AND POS-DERIVED FEATURES FOR IMAGE BLUR DETECTION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 6, 2016): 1051–55. http://dx.doi.org/10.5194/isprsarchives-xli-b1-1051-2016.

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The image quality plays an important role for Unmanned Aerial Vehicle (UAV)’s applications. The small fixed wings UAV is suffering from the image blur due to the crosswind and the turbulence. Position and Orientation System (POS), which provides the position and orientation information, is installed onto an UAV to enable acquisition of UAV trajectory. It can be used to calculate the positional and angular velocities when the camera shutter is open. This study proposes a POS-assisted method to detect the blur image. The major steps include feature extraction, blur image detection and verification. In feature extraction, this study extracts different features from images and POS. The image-derived features include mean and standard deviation of image gradient. For POS-derived features, we modify the traditional degree-of-linear-blur (blinear) method to degree-of-motion-blur (bmotion) based on the collinear condition equations and POS parameters. Besides, POS parameters such as positional and angular velocities are also adopted as POS-derived features. In blur detection, this study uses Support Vector Machines (SVM) classifier and extracted features (i.e. image information, POS data, blinear and bmotion) to separate blur and sharp UAV images. The experiment utilizes SenseFly eBee UAV system. The number of image is 129. In blur image detection, we use the proposed degree-of-motion-blur and other image features to classify the blur image and sharp images. The classification result shows that the overall accuracy using image features is only 56%. The integration of image-derived and POS-derived features have improved the overall accuracy from 56% to 76% in blur detection. Besides, this study indicates that the performance of the proposed degree-of-motion-blur is better than the traditional degree-of-linear-blur.
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Teo, Tee-Ann, and Kai-Zhi Zhan. "INTEGRATION OF IMAGE-DERIVED AND POS-DERIVED FEATURES FOR IMAGE BLUR DETECTION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 6, 2016): 1051–55. http://dx.doi.org/10.5194/isprs-archives-xli-b1-1051-2016.

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The image quality plays an important role for Unmanned Aerial Vehicle (UAV)’s applications. The small fixed wings UAV is suffering from the image blur due to the crosswind and the turbulence. Position and Orientation System (POS), which provides the position and orientation information, is installed onto an UAV to enable acquisition of UAV trajectory. It can be used to calculate the positional and angular velocities when the camera shutter is open. This study proposes a POS-assisted method to detect the blur image. The major steps include feature extraction, blur image detection and verification. In feature extraction, this study extracts different features from images and POS. The image-derived features include mean and standard deviation of image gradient. For POS-derived features, we modify the traditional degree-of-linear-blur (blinear) method to degree-of-motion-blur (bmotion) based on the collinear condition equations and POS parameters. Besides, POS parameters such as positional and angular velocities are also adopted as POS-derived features. In blur detection, this study uses Support Vector Machines (SVM) classifier and extracted features (i.e. image information, POS data, blinear and bmotion) to separate blur and sharp UAV images. The experiment utilizes SenseFly eBee UAV system. The number of image is 129. In blur image detection, we use the proposed degree-of-motion-blur and other image features to classify the blur image and sharp images. The classification result shows that the overall accuracy using image features is only 56%. The integration of image-derived and POS-derived features have improved the overall accuracy from 56% to 76% in blur detection. Besides, this study indicates that the performance of the proposed degree-of-motion-blur is better than the traditional degree-of-linear-blur.
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Kaur, Sukhamrit, and Dr Vijay Kumar Banga. "Hybrid fuzzy and support vector machine based blur detection technique." International Journal of Engineering & Technology 7, no. 4.5 (2018): 591. http://dx.doi.org/10.14419/ijet.v7i4.5.21164.

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The main objective of our research work is to acquire good quality of an image which is blurred. Therefore, in this research our effort is to propose an advanced algorithm to improve the quality of an image by eliminate the blur in an efficient manner. In this paper, two types of blurred images (i.e., Gaussian blur and out of focus) are used. Deblurring techniques are mostly used to eradicate the blur of an image using different methods & parameters. To reimburse blur different types of methods like algorithm, filtering techniques, fuzzy based approach, support vector machine are used. Blur detection methods are used to eradicate the blur from a blurred section of an image which is caused by the out of focus blur and Gaussian blur.
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Jha, Tantra Nath. "Velocity Detection from a Motion Blur Image Using Radon Transformation." Tribhuvan University Journal 32, no. 2 (2018): 243–48. http://dx.doi.org/10.3126/tuj.v32i2.24721.

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Motion blur is the result when the camera shutter remains open for an extended period of time and a relative motion between camera and object occurs. An approach for velocity detection based on motion blurred images has been implemented by the Radon transformation. The motion blur parameters are first estimated from the acquired images by using Radon transformation and then used to detect the speed of the moving object in the scene. Here established a link between the motion blur information of a 2D image and camera manufacturer’s data sheet and its calibration
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Koik, Boon Tatt, and Haidi Ibrahim. "Thumbnail with Integrated Blur Based on Edge Width Analysis." Journal of Sensors 2016 (2016): 1–16. http://dx.doi.org/10.1155/2016/5803095.

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Thumbnail image is widely used in electronic devices to help the user to scan through original high resolution images. Hence, it is essential to represent the thumbnail image correspondingly to the original image. A blur image should not appear to be a clear image in thumbnail form, where this situation might mislead the perceptual analysis of user. The main purpose of this research work is to develop a downsampling algorithm to create a thumbnail image which includes blur information. The proposed method has three stages involved to obtain the proposed output thumbnail, which are preliminary processes, blur detection, and lastly image downsampling. For preliminary processes, Sobel first-order derivatives, gradient magnitude, and gradient orientation are determined. In blur detection stage, local maximum, local minimum, and gradient orientation are ultilized to calculate the edge width. The thumbnail image with blur information is generated using the average edge width map as a weightage to integrate blur information. This proposed method has achieved satisfying results and has high potential to be applied as one of the thumbnail generation options for photo viewing.
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Xiao, Xiao, Fan Yang, and Amir Sadovnik. "MSDU-Net: A Multi-Scale Dilated U-Net for Blur Detection." Sensors 21, no. 5 (2021): 1873. http://dx.doi.org/10.3390/s21051873.

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A blur detection problem which aims to separate the blurred and clear regions of an image is widely used in many important computer vision tasks such object detection, semantic segmentation, and face recognition, attracting increasing attention from researchers and industry in recent years. To improve the quality of the image separation, many researchers have spent enormous efforts on extracting features from various scales of images. However, the matter of how to extract blur features and fuse these features synchronously is still a big challenge. In this paper, we regard blur detection as an image segmentation problem. Inspired by the success of the U-net architecture for image segmentation, we propose a multi-scale dilated convolutional neural network called MSDU-net. In this model, we design a group of multi-scale feature extractors with dilated convolutions to extract textual information at different scales at the same time. The U-shape architecture of the MSDU-net can fuse the different-scale texture features and generated semantic features to support the image segmentation task. We conduct extensive experiments on two classic public benchmark datasets and show that the MSDU-net outperforms other state-of-the-art blur detection approaches.
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Yang, Dong, and Shiyin Qin. "Restoration of Partial Blurred Image Based on Blur Detection and Classification." Journal of Electrical and Computer Engineering 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/2374926.

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A new restoration algorithm for partial blurred image which is based on blur detection and classification is proposed in this paper. Firstly, a new blur detection algorithm is proposed to detect the blurred regions in the partial blurred image. Then, a new blur classification algorithm is proposed to classify the blurred regions. Once the blur class of the blurred regions is confirmed, the structure of the blur kernels of the blurred regions is confirmed. Then, the blur kernel estimation methods are adopted to estimate the blur kernels. In the end, the blurred regions are restored using nonblind image deblurring algorithm and replace the blurred regions in the partial blurred image with the restored regions. The simulated experiment shows that the proposed algorithm performs well.
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Ramadhan, Zamen Abood, and Dhia Alzubaydi. "Text Detection in Natural Image By Connected Component Labeling." Al-Mustansiriyah Journal of Science 30, no. 1 (2019): 111. http://dx.doi.org/10.23851/mjs.v30i1.531.

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The process of detect the text from the natural image is complex and difficult process because the variance by the devises that take the images and different the texts that found in images in the orientation, size and style. Given the importance the texts in images in the several of application of computer vision. In this paper dependent on the spatial natural images and on the spatial data set for the street sign that include the texts by the different size and different orientation. In this paper detected the texts in images by using robust method by using several algorithms, at the first stage making preprocessing for the image to blur the image and reduce the nose on it by Gaussian blur, second stage making processing that include canny edge detection to detect the edges and dilation, third stage applying connected component to filling all objects in image then applying stroke width transform(SWT) to detect the letter candidate and applying the system on the several images that include different types of texts.
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Zhou Yuan, 周圆, 王凯 Wang Kai, 张皓翔 Zhang Haoxiang, 许文强 Xu Wenqiang, and 李龙 Li Long. "Blur Image Quality Assessment Method Based on Blur Detection Probability Variation." Laser & Optoelectronics Progress 57, no. 10 (2020): 101004. http://dx.doi.org/10.3788/lop57.101004.

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Dissertations / Theses on the topic "Image Blur Detection"

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Andhavarapu, Sarat Kiran. "Image Blur Detection with Two-Dimensional Haar Wavelet Transform." DigitalCommons@USU, 2015. https://digitalcommons.usu.edu/etd/4443.

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Efficient detection of image blur and its extent is an open research problem in computer vision. Image blur has a negative impact on image quality. Blur is introduced into images due to various factors including limited contrast, improper exposure time or unstable device handling. Toward this end, an algorithm is presented for image blur detection with the use of Two-Dimensional Haar Wavelet transform (2D HWT). The algorithm is experimentally compared with two other image blur detection algorithms frequently cited in the literature. When evaluated over a sample of images, the algorithm performed on par or better than the two other blur detection algorithms.
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Sieberth, Till. "Motion blur in digital images : analys, detection and correction of motion blur in photogrammetry." Thesis, Loughborough University, 2016. https://dspace.lboro.ac.uk/2134/20212.

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Unmanned aerial vehicles (UAV) have become an interesting and active research topic for photogrammetry. Current research is based on images acquired by an UAV, which have a high ground resolution and good spectral and radiometrical resolution, due to the low flight altitudes combined with a high resolution camera. UAV image flights are also cost effective 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 detection and removal of these images is currently achieved manually, which is both time consuming and prone to error, particularly for large image-sets. To increase the quality of data processing an automated process is necessary, which must be both reliable and quick. This thesis proves the negative affect that blurred images have on photogrammetric processing. It shows that small amounts of blur do have serious impacts on target detection and that it slows down processing speed due to the requirement of human intervention. Larger blur can make an image completely unusable and needs to be excluded from processing. To exclude images out of large image datasets an algorithm was developed. The newly developed method makes it possible to detect blur caused by linear camera displacement. The method is based on human detection of blur. Humans detect blurred images best by comparing it to other images in order to establish whether an image is blurred or not. The developed algorithm simulates this procedure by creating an image for comparison using image processing. Creating internally a comparable image makes the method independent of additional images. However, the calculated blur value named SIEDS (saturation image edge difference standard-deviation) on its own does not provide an absolute number to judge if an image is blurred or not. To achieve a reliable judgement of image sharpness the SIEDS value has to be compared to other SIEDS values of the same dataset. This algorithm enables the exclusion of blurred images and subsequently allows photogrammetric processing without them. However, it is also possible to use deblurring techniques to restor blurred 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. An algorithm based on the Fourier transformation is presented. This works well in flat areas, but the need for geometrically correct sharp images for deblurring may limit the application. Another method to enhance the image is the unsharp mask method, which improves images significantly and makes photogrammetric processing more successful. However, deblurring of images needs to focus on geometric correct deblurring to assure geometric correct measurements. Furthermore, a novel edge shifting approach was developed which aims to do geometrically correct deblurring. The idea of edge shifting appears to be promising but requires more advanced programming.
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Ghosh, Roy Gourab. "A Simple Second Derivative Based Blur Estimation Technique." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1366890068.

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Marais, Izak van Zyl. "On-board image quality assessment for a satellite." Thesis, Stellenbosch : University of Stellenbosch, 2009. http://hdl.handle.net/10019.1/1436.

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Thesis (PhD (Electronic Engineering))--University of Stellenbosch, 2009.<br>The downloading of images is a bottleneck in the image acquisition chain for low earth orbit, remote sensing satellites. An on-board image quality assessment system could optimise use of available downlink time by prioritising images for download, based on their quality. An image quality assessment system based on measuring image degradations is proposed. Algorithms for estimating degradations are investigated. The degradation types considered are cloud cover, additive sensor noise and the defocus extent of the telescope. For cloud detection, the novel application of heteroscedastic discriminant analysis resulted in better performance than comparable dimension reducing transforms from remote sensing literature. A region growing method, which was previously used on-board a micro-satellite for cloud cover estimation, is critically evaluated and compared to commonly used thresholding. The thresholding method is recommended. A remote sensing noise estimation algorithm is compared to a noise estimation algorithm based on image pyramids. The image pyramid algorithm is recommended. It is adapted, which results in smaller errors. A novel angular spectral smoothing method for increasing the robustness of spectral based, direct defocus estimation is introduced. Three existing spectral based defocus estimation methods are compared with the angular smoothing method. An image quality assessment model is developed that models the mapping of the three estimated degradation levels to one quality score. A subjective image quality evaluation experiment is conducted, during which more than 18000 independent human judgements are collected. Two quality assessment models, based on neural networks and splines, are tted to this data. The spline model is recommended. The integrated system is evaluated and image quality predictions are shown to correlate well with human quality perception.
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Semerák, Jakub. "Hromadné generování grafických prezentací." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2016. http://www.nusl.cz/ntk/nusl-255334.

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This thesis describes design and implementation of system that allows batch generation of graphical presentations. The system also includes modules for image quality evaluation using no-reference blur metric and salient object detection. Selected methods for evaluation of image quality are described in detail and implemented in corresponding chapters, including proposed modifications and changes. Blur detection is based on wavelet transform, and salient object detection is achieved by investigating image contrast. Capabilities of these modules are evaluated on suitable image datasets.
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Thiel, Stefan U. "The use of image processing techniques for the automated detection of blue-green algae." Thesis, University of South Wales, 1994. https://pure.southwales.ac.uk/en/studentthesis/the-use-of-image-processing-techniques-for-the-automated-detection-of-bluegreen-algae(fd73551d-72d8-46a1-a0e8-e3c08b51f03e).html.

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The determination of water quality in freshwater lakes and reservoirs is an important task, which must be carried out on a regular basis. Information about long term water quality must be provided by the existence of particular organisms, for example blue-green algae. Currently the detection of these algae is done in a very time consuming manual way, involving highly trained biologists, for example those employed by the National Rivers Authority. This thesis is a first investigation in the automatic detection of blue-green algae using image processing techniques. Samples of seven species of blue-green algae and two species of green algae were examined under a microscope and transferred to a computer. The micro­ scope pictures were then stored as digital images. In order to locate the organisms Image Segmentation routines were applied. In particular, a newly developed LoG Thresholding Operator proved to be effective for the segmentation of biological organisms. Image Enhancement improved the quality and appearance of the segmented species in the images. In the identification process the biological key, which describes some important features of each species, needed to be implemented. With the aid of shape algorithms and textural algorithms both occluding and non-occluding organisms were analyzed and meaningful features were extracted. The obtained features were then used to classify the organisms into different Species. Both, discriminant analysis and neural networks were used for classification purposes. A detection rate of approximately 90% was achieved. The approach has produced promising results and it is hoped that further Investigations will be encouraged.
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"Image partial blur detection and classification." 2008. http://library.cuhk.edu.hk/record=b5893527.

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Liu, Renting.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.<br>Includes bibliographical references (leaves 40-46).<br>Abstracts in English and Chinese.<br>Chapter 1 --- Introduction --- p.1<br>Chapter 2 --- Related Work and System Overview --- p.6<br>Chapter 2.1 --- Previous Work in Blur Analysis --- p.6<br>Chapter 2.1.1 --- Blur detection and estimation --- p.6<br>Chapter 2.1.2 --- Image deblurring --- p.8<br>Chapter 2.1.3 --- Low DoF image auto-segmentation --- p.14<br>Chapter 2.2 --- System Overview --- p.15<br>Chapter 3 --- Blur Features and Classification --- p.18<br>Chapter 3.1 --- Blur Features --- p.18<br>Chapter 3.1.1 --- Local Power Spectrum Slope --- p.19<br>Chapter 3.1.2 --- Gradient Histogram Span --- p.21<br>Chapter 3.1.3 --- Maximum Saturation --- p.24<br>Chapter 3.1.4 --- Local Autocorrelation Congruency --- p.25<br>Chapter 3.2 --- Classification --- p.28<br>Chapter 4 --- Experiments and Results --- p.29<br>Chapter 4.1 --- Blur Patch Detection --- p.29<br>Chapter 4.2 --- Blur degree --- p.33<br>Chapter 4.3 --- Blur Region Segmentation --- p.34<br>Chapter 5 --- Conclusion and Future Work --- p.38<br>Bibliography --- p.40<br>Chapter A --- Blurred Edge Analysis --- p.47
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"Image blur detection, estimation and analysis." 2015. http://repository.lib.cuhk.edu.hk/en/item/cuhk-1291736.

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Shi, Jianping.<br>Thesis Ph.D. Chinese University of Hong Kong 2015.<br>Includes bibliographical references (leaves 118-130).<br>Abstracts also in Chinese.<br>Title from PDF title page (viewed on 08, November, 2016).
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"New Signal Processing Methods for Blur Detection and Applications." Doctoral diss., 2019. http://hdl.handle.net/2286/R.I.54945.

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abstract: The depth richness of a scene translates into a spatially variable defocus blur in the acquired image. Blurring can mislead computational image understanding; therefore, blur detection can be used for selective image enhancement of blurred regions and the application of image understanding algorithms to sharp regions. This work focuses on blur detection and its application to image enhancement. This work proposes a spatially-varying defocus blur detection based on the quotient of spectral bands; additionally, to avoid the use of computationally intensive algorithms for the segmentation of foreground and background regions, a global threshold defined using weak textured regions on the input image is proposed. Quantitative results expressed in the precision-recall space as well as qualitative results overperform current state-of-the-art algorithms while keeping the computational requirements at competitive levels. Imperfections in the curvature of lenses can lead to image radial distortion (IRD). Computer vision applications can be drastically affected by IRD. This work proposes a novel robust radial distortion correction algorithm based on alternate optimization using two cost functions tailored for the estimation of the center of distortion and radial distortion coefficients. Qualitative and quantitative results show the competitiveness of the proposed algorithm. Blur is one of the causes of visual discomfort in stereopsis. Sharpening applying traditional algorithms can produce an interdifference which causes eyestrain and visual fatigue for the viewer. A sharpness enhancement method for stereo images that incorporates binocular vision cues and depth information is presented. Perceptual evaluation and quantitative results based on the metric of interdifference deviation are reported; results of the proposed algorithm are competitive with state-of-the-art stereo algorithms. Digital images and videos are produced every day in astonishing amounts. Consequently, the market-driven demand for higher quality content is constantly increasing which leads to the need of image quality assessment (IQA) methods. A training-free, no-reference image sharpness assessment method based on the singular value decomposition of perceptually-weighted normalized-gradients of relevant pixels in the input image is proposed. Results over six subject-rated publicly available databases show competitive performance when compared with state-of-the-art algorithms.<br>Dissertation/Thesis<br>Doctoral Dissertation Electrical Engineering 2019
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Su, Mei-Yun, and 蘇美云. "Blur Detection and Inpainting for Endoscopic Images." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/9hma7n.

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碩士<br>義守大學<br>生物醫學工程學系<br>102<br>There are two types of endoscopy, flexible and rigid. Flexible endoscopes are used to inspect the esophagus, stomach, small bowel and colon, whereas rigid endoscopes are used for a variety of minimally invasive surgeries. These endoscopes come in various sizes, but all have a tiny video camera at the tip. The images inside bodies from endoscopy can provide an immediate observation and help diagnosis and therapy. However the images could have a phenomenon that is called out-of-focus. Moreover, the tissue cannot be observed effectively if specular reflection appears. Therefore, the purpose of this study is blur image detection and inpainting the non-blur images with specular reflection. Blur image detection is used to distinguish between focus and out of focus images. Then the focus images with specular reflection are inpainted by using example-based method. First, the image features were extracted by two texture analysis techniques: GLCM (Grey Level Co-occurrence Matrix) and Laws’ texture measures. Then used seven features based GLCM and absolute central moment based on Laws’ texture meatures to classify the images into two classes: blur and non-blur by k-means clustering. We evaluate several combinations of features in the experiment. Gaussian filter was used to produce 70 degrees of blurring with seven mask sizes and ten standard deviations to have ground truths of the test images. After classification we found the accuracy is at least 77% for GLCM-based and 54% for Laws’-based features. In addition, accuracy of 95% and 85% is achieved in most combinations, respectively. The obtained non-blur images with specular reflection were then segmented and inpainted. The experimental results show that the specular reflection can be successfully inpainted.
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Books on the topic "Image Blur Detection"

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Thiel, Stefan U. The use of image processing techniques for the automated detection of blue-green algae. 1994.

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Book chapters on the topic "Image Blur Detection"

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Williams, Bryan M., Baidaa Al-Bander, Harry Pratt, et al. "Fast Blur Detection and Parametric Deconvolution of Retinal Fundus Images." In Fetal, Infant and Ophthalmic Medical Image Analysis. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67561-9_22.

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Miyamoto, Ryusuke, and Shingo Kobayashi. "Object Detection Based on Image Blur Using Spatial-Domain Filtering with Haar-Like Features." In Advances in Visual Computing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50835-1_28.

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Madooei, Ali, Mark S. Drew, Maryam Sadeghi, and M. Stella Atkins. "Automatic Detection of Blue-White Veil by Discrete Colour Matching in Dermoscopy Images." In Advanced Information Systems Engineering. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40760-4_57.

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Kropidlowski, Karol, Marcin Kociolek, Michal Strzelecki, and Dariusz Czubinski. "Blue Whitish Veil, Atypical Vascular Pattern and Regression Structures Detection in Skin Lesions Images." In Computer Vision and Graphics. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46418-3_37.

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Rajagopal, Sivakumar, and Babu Gopal. "Effective and Accurate Diagnosis Using Brain Image Fusion." In Applications of Deep Learning and Big IoT on Personalized Healthcare Services. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2101-4.ch012.

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Medical imaging techniques are routinely employed to create images of the human system for clinical purposes. Multi-modality medical imaging is a widely used technology for diagnosis, detection, and prediction of various tissue abnormalities. This chapter is focused on the development of an improved brain image processing technique for the removal of noise from a magnetic resonance image (MRI) for accurate image restoration. Feature selection and extraction of MRI brain images are processed using image fusion. The medical images suffer from motion blur and noise for which image denoising is developed through non-local means (NLM) filtering for smoothing and shrinkage rule for sharpening. The peak signal to noise ratio (PSNR) of improved curvelet based self-similarity NLM method is better than discrete wavelet transform with an NLM filter.
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Soundrapandiyan, Rajkumar, and Ramani Selvanambi. "A Proficient Hybrid Framework for Image Retrieval." In Applications of Artificial Intelligence for Smart Technology. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3335-2.ch014.

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In this work, an image retrieval system based on three main factors is constructed. The proposed system at first chooses relevant pictures from an enormous information base utilizing colour moment data. Accordingly, canny edge recognition and local binary pattern and strategies are utilized to remove the texture plus edge separately, as of the uncertainty and resultant pictures of the underlying phase of the system. Afterward, the chi-square distance between the red-green and the blue colour channels of the query and the main image are calculated. Then these two (the LBP pattern and the edge feature extracted from the canny edge detection and by chi-square method) data about these two highlights compared to the uncertainty and chosen pictures are determined and consolidated, are then arranged and the nearest ‘n' images are presented. Two datasets, Wang and the Corel databases, are used in this work. The results shown herein are obtained using the Wang dataset. The Wang dataset contains 1,000 images and Corel contains 10,000 images.
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Poliarus, Oleksandr, and Yevhen Poliakov. "Detection of Landmarks by Mobile Autonomous Robots Based on Estimating the Color Parameters of the Surrounding Area." In Examining Optoelectronics in Machine Vision and Applications in Industry 4.0. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-6522-3.ch008.

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Remote detection of landmarks for navigation of mobile autonomous robots in the absence of GPS is carried out by low-power radars, ultrasonic and laser rangefinders, night vision devices, and also by video cameras. The aim of the chapter is to develop the method for landmarks detection using the color parameters of images. For this purpose, the optimal system of stochastic differential equations was synthesized according to the criterion of the generalized variance minimum, which allows to estimate the color intensity (red, green, blue) using a priori information and current measurements. The analysis of classical and nonparametric methods of landmark detection, as well as the method of optimal estimation of color parameters jumps is carried out. It is shown that high efficiency of landmark detection is achieved by nonparametric estimating the first Hilbert-Huang modes of decomposition of the color parameters distribution.
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Osmanovic, Smajo. "Imaging." In Glaucoma. Oxford University Press, 2012. http://dx.doi.org/10.1093/oso/9780199757084.003.0010.

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•Stereoscopic optic nerve photography has been considered the gold standard for documenting the optic nerve head (ONH)/retinal nerve fiber layer (RNFL) status. •Digital imaging technologies were developed recently to provide reproducible and more objective quantitative assessment of the ONH and RNFL. •Each technology measures different aspects of ONH and RNFL morphology; information obtained from different imaging devices is complementary and can be used to detect different abnormal features in the same patient. •Possible roles of ONH/RNFL imaging in clinical practice: •Documentation of ONH status• Glaucoma diagnosis •Detecting progression •Risk assessment •Screening for glaucoma •CSLO is based on a method of confocal imaging. •A diode laser (670 nm) (Fig 5.1A) scans the surface of posterior pole horizontally and vertically (x- and y-axes) with high speed. Reflected light is detected by a sensor after being filtered by a confocal pinhole which is conjugate to the focal plane of the retina. •By shifting the confocal pinhole, a series of planar scans are acquired at increasing depths and after alignment are combined to create 3-D topographic map of the retina and ONH surface. •Commercially available CSLO devices with major features are listed in Table 5.1. •Image acquisition with Heidelberg Retinal Tomography (HRT) is fast; single tomographic slices are captured in only 24 ms (faster than involuntary saccades or fixation movements). •Pupillary dilation is not needed. •Good images require adequate patient positioning, good fixation, clear media, appropriate focus, and centering the optic nerve in the image. •16 to 64 planar scans are acquired per set. Unusable scans are replaced by software until three useful sets are obtained. •The operator defines the optic disc margin by drawing a contour line along scleral ring. • A reference plane is determined by the HRT software 50 μm below the average height of the contour line in the inferior temporal quadrant. All structures above the reference plane and within the contour line are defined as a neuroretinal rim and are shown as blue (sloped) and green (flat) areas on the topography image (Fig 5.2c).
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Conference papers on the topic "Image Blur Detection"

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Renting Liu, Zhaorong Li, and Jiaya Jia. "Image partial blur detection and classification." In 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2008. http://dx.doi.org/10.1109/cvpr.2008.4587465.

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Lee, Seungkyu, Hyunjung Shim, James D. K. Kim, and Chang Yeong Kim. "ToF depth image motion blur detection using 3D blur shape models." In IS&T/SPIE Electronic Imaging, edited by Charles A. Bouman, Ilya Pollak, and Patrick J. Wolfe. SPIE, 2012. http://dx.doi.org/10.1117/12.908055.

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Badique, E., N. Ohyama, M. Yachida, T. Honda, and J. Tsujiuchi. "Correction of motion blur in CCD color endoscope images." In International Topical Meeting on Image Detection and Quality, edited by Lucien F. Guyot. SPIE, 1987. http://dx.doi.org/10.1117/12.966744.

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Pendyala, Siddartha, Pradeep Ramesha, Akshay Veer Bns, and Dheeraj Arora. "Blur detection and fast blind image deblurring." In 2015 Annual IEEE India Conference (INDICON). IEEE, 2015. http://dx.doi.org/10.1109/indicon.2015.7443562.

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Bora, R. M., and N. M. Shahane. "Image forgery detection through motion blur estimates." In 2012 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE, 2012. http://dx.doi.org/10.1109/iccic.2012.6510180.

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Amrutha, S., and Manju Manuel. "Blur type inconsistency based image tampering detection." In 2017 International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2017. http://dx.doi.org/10.1109/icoei.2017.8300814.

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Ni, Karl S., Zachary Z. Sun, and Nadya T. Bliss. "Real-time global motion blur detection." In 2012 19th IEEE International Conference on Image Processing (ICIP 2012). IEEE, 2012. http://dx.doi.org/10.1109/icip.2012.6467556.

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Punnappurath, Abhijith, A. N. Rajagopalan, and Guna Seetharaman. "Registration and occlusion detection in motion blur." In 2013 20th IEEE International Conference on Image Processing (ICIP). IEEE, 2013. http://dx.doi.org/10.1109/icip.2013.6738519.

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Yang, ChangLong, XiaoLin Liu, JiaKai Dai, and Wei Chen. "Multiscale spatially-varying blur detection and extraction." In 2018 International Conference on Image, Video Processing and Artificial Intelligence, edited by Ruidan Su. SPIE, 2018. http://dx.doi.org/10.1117/12.2502098.

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Chong, Rachel Mabanag, and Toshihisa Tanaka. "Image Extrema Analysis and Blur Detection with Identification." In 2008 IEEE International Conference on Signal Image Technology and Internet Based Systems (SITIS). IEEE, 2008. http://dx.doi.org/10.1109/sitis.2008.38.

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