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Journal articles on the topic 'Defocus blur estimation'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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1

Ishihara, Shin, Antonin Sulc, and Imari Sato. "Depth estimation using spectrally varying defocus blur." Journal of the Optical Society of America A 38, no. 8 (2021): 1140. http://dx.doi.org/10.1364/josaa.422059.

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2

Karaali, Ali, and Claudio Rosito Jung. "Edge-Based Defocus Blur Estimation With Adaptive Scale Selection." IEEE Transactions on Image Processing 27, no. 3 (2018): 1126–37. http://dx.doi.org/10.1109/tip.2017.2771563.

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3

Chang, Chia-Feng, Jiunn-Lin Wu, and Ting-Yu Tsai. "A Single Image Deblurring Algorithm for Nonuniform Motion Blur Using Uniform Defocus Map Estimation." Mathematical Problems in Engineering 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/6089650.

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One of the most common artifacts in digital photography is motion blur. When capturing an image under dim light by using a handheld camera, the tendency of the photographer’s hand to shake causes the image to blur. In response to this problem, image deblurring has become an active topic in computational photography and image processing in recent years. From the view of signal processing, image deblurring can be reduced to a deconvolution problem if the kernel function of the motion blur is assumed to be shift invariant. However, the kernel function is not always shift invariant in real cases;

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4

Yang, Haoyuan, Xiuqin Su, and Songmao Chen. "Blind Image Deconvolution Algorithm Based on Sparse Optimization with an Adaptive Blur Kernel Estimation." Applied Sciences 10, no. 7 (2020): 2437. http://dx.doi.org/10.3390/app10072437.

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Image blurs are a major source of degradation in an imaging system. There are various blur types, such as motion blur and defocus blur, which reduce image quality significantly. Therefore, it is essential to develop methods for recovering approximated latent images from blurry ones to increase the performance of the imaging system. In this paper, an image blur removal technique based on sparse optimization is proposed. Most existing methods use different image priors to estimate the blur kernel but are unable to fully exploit local image information. The proposed method adopts an image prior b

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5

Lin, Huei-Yung, Chin-Chen Chang, and Xin-Han Chou. "No-reference objective image quality assessment using defocus blur estimation." Journal of the Chinese Institute of Engineers 40, no. 4 (2017): 341–46. http://dx.doi.org/10.1080/02533839.2017.1314193.

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6

Zhang, Xinxin, Ronggang Wang, Xiubao Jiang, Wenmin Wang, and Wen Gao. "Spatially variant defocus blur map estimation and deblurring from a single image." Journal of Visual Communication and Image Representation 35 (February 2016): 257–64. http://dx.doi.org/10.1016/j.jvcir.2016.01.002.

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7

Pan, Rongjiang. "Defocus blur estimation in calibrated multi-view images for 3D archaeological documentation." Digital Applications in Archaeology and Cultural Heritage 14 (September 2019): e00109. http://dx.doi.org/10.1016/j.daach.2019.e00109.

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8

Zia, Ali, Jun Zhou, and Yongsheng Gao. "Exploring Chromatic Aberration and Defocus Blur for Relative Depth Estimation From Monocular Hyperspectral Image." IEEE Transactions on Image Processing 30 (2021): 4357–70. http://dx.doi.org/10.1109/tip.2021.3071682.

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9

Deschênes, F., D. Ziou, and P. Fuchs. "Improved estimation of defocus blur and spatial shifts in spatial domain: a homotopy-based approach." Pattern Recognition 36, no. 9 (2003): 2105–25. http://dx.doi.org/10.1016/s0031-3203(03)00040-2.

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10

Deschênes, F., D. Ziou, and P. Fuchs. "An unified approach for a simultaneous and cooperative estimation of defocus blur and spatial shifts." Image and Vision Computing 22, no. 1 (2004): 35–57. http://dx.doi.org/10.1016/j.imavis.2003.08.003.

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11

Karam, Ghada Sabah. "Blurred Image Restoration with Unknown Point Spread Function." Al-Mustansiriyah Journal of Science 29, no. 1 (2018): 189. http://dx.doi.org/10.23851/mjs.v29i1.335.

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Blurring image caused by a number of factors such as de focus, motion, and limited sensor resolution. Most of existing blind deconvolution research concentrates at recovering a single blurring kernel for the entire image. We proposed adaptive blind- non reference image quality assessment method for estimation the blur function (i.e. point spread function PSF) from the image acquired under low-lighting conditions and defocus images using Bayesian Blind Deconvolution. It is based on predicting a sharp version of a blurry inter image and uses the two images to solve a PSF. The estimation down by

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12

Verhoeven, G. J. "FOCUSING ON OUT-OF-FOCUS: ASSESSING DEFOCUS ESTIMATION ALGORITHMS FOR THE BENEFIT OF AUTOMATED IMAGE MASKING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2 (May 30, 2018): 1149–56. http://dx.doi.org/10.5194/isprs-archives-xlii-2-1149-2018.

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Acquiring photographs as input for an image-based modelling pipeline is less trivial than often assumed. Photographs should be correctly exposed, cover the subject sufficiently from all possible angles, have the required spatial resolution, be devoid of any motion blur, exhibit accurate focus and feature an adequate depth of field. The last four characteristics all determine the “sharpness” of an image and the photogrammetric, computer vision and hybrid photogrammetric computer vision communities all assume that the object to be modelled is depicted “acceptably” sharp throughout the whole imag

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13

Naqvi, Syed Faraz Ahmed, Kamal Niwaria, and Bharti Chourasia. "An Edge Based Technique for Spatially Varying Defocus Blur Estimation of Image using Gradient Magnitudes - Simulation and Result." International Journal of Computer Sciences and Engineering 7, no. 10 (2019): 81–89. http://dx.doi.org/10.26438/ijcse/v7i10.8189.

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14

Tiwari, Shamik. "A Blind Restoration Approach for Defocused Barcode Images." International Journal of Strategic Information Technology and Applications 8, no. 3 (2017): 41–58. http://dx.doi.org/10.4018/ijsita.2017070103.

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Use of a mobile camera for barcode decoding provides high portability and availability but it requires that the recorded barcode image must be accurate representation of the barcode that is available on the product. Barcode scanning is challenging because images may be degraded due to out-of-focus blur at the time of image acquisition. Therefore, image restoration is essential in making image sharp and useful. In case of blind restoration of such barcode images accurate estimation of out-of-focus blur parameter is highly desirable. In this article, a robust method has been proposed for estimat

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15

Zhu, Xiang, Scott Cohen, Stephen Schiller, and Peyman Milanfar. "Estimating Spatially Varying Defocus Blur From A Single Image." IEEE Transactions on Image Processing 22, no. 12 (2013): 4879–91. http://dx.doi.org/10.1109/tip.2013.2279316.

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16

Kulkarni, Jyoti B., and C. M. SheelaRani. "Depth Estimation from Defocused Images: A Survey." International Journal of Advances in Applied Sciences 7, no. 3 (2018): 220. http://dx.doi.org/10.11591/ijaas.v7.i3.pp220-225.

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<p>An important step in 3D data generation is the generation of depth map. Depth map is a black and white image which has exactly the same size of the original captured 2D image that indicates the relative distance of each pixel from the observer to the objects in the real world. This paper presents a survey of Depth Perception from Defocused or blurs images as well as image from motion. The change of distance of the object from the camera has direct relation with the amount of blurring of object in the image. The amount of blurring will be calculated with a comparison in front of the ca

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17

Kumar, Himanshu, Sumana Gupta, and K. S. Venkatesh. "Simultaneous Estimation of Defocus and Motion Blurs From Single Image Using Equivalent Gaussian Representation." IEEE Transactions on Circuits and Systems for Video Technology 30, no. 10 (2020): 3571–83. http://dx.doi.org/10.1109/tcsvt.2019.2944915.

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18

AHN, Sangwoo, and Jongwha CHONG. "A Novel Search Approach for Blur Kernel Estimation of Defocused Image Restoration." IEICE Transactions on Information and Systems E96.D, no. 3 (2013): 754–57. http://dx.doi.org/10.1587/transinf.e96.d.754.

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19

HOLEVA, LEE F. "RANGE ESTIMATION FROM CAMERA BLUR BY REGULARIZED ADAPTIVE IDENTIFICATION." International Journal of Pattern Recognition and Artificial Intelligence 08, no. 06 (1994): 1273–300. http://dx.doi.org/10.1142/s0218001494000644.

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One of the fundamental problems of machine vision is the estimation of object depth from perceived images. This paper describes both an apparatus and the corresponding algorithms for the passive extraction of object depth. Here passive extraction implies the processing of images acquired using only the existing illumination, in this case roughly uniform white light. Depth from defocused algorithms are extremely sensitive to image variations. Regularization, the application of a priori constraints, is employed to improve the accuracy of the range measurements. When the camera’s point spread fun

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20

Tian, Mingming, Guisheng Liao, Shengqi Zhu, Xiongpeng He, Yongjun Liu, and Yunpeng Li. "An Efficient Method for Ground Maneuvering Target Refocusing and Motion Parameter Estimation Based on DPT–KT–MFP." Remote Sensing 13, no. 6 (2021): 1092. http://dx.doi.org/10.3390/rs13061092.

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The image of ground maneuvering targets may be defocused due to the Doppler ambiguity, high-order range migration (RM), and Doppler frequency migration (DFM) caused by the target’s complex motions in a synthetic aperture radar (SAR) system. To settle these problems, an efficient algorithm based on discrete polynomial-phase transform (DPT), keystone transform (KT), and matched filtering processing (MFP) is presented for ground maneuvering target refocusing and motion parameter estimation in this paper. Firstly, the DPT is applied to transform the cubic phase into the quadratic phase and simulta

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21

Goudail, François, Olivier Ruch, and Philippe Réfrégier. "Deconvolution of several versions of a scene perturbed by different defocus blurs: influence of kernel diameters on restoration quality and on robustness to kernel estimation." Applied Optics 39, no. 35 (2000): 6602. http://dx.doi.org/10.1364/ao.39.006602.

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22

"Defocus Blur Detection and Estimation from Imaging Sensors." Sensors 18, no. 4 (2018): 1135. http://dx.doi.org/10.3390/s18041135.

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