Detection of motion blur direction based on maxima locations for blind deconvolution

2011 ◽  
Author(s):  
Rachel M. Chong ◽  
Toshihisa Tanaka
Keyword(s):  
Blind Deconvolution ◽  
Motion Blur ◽  
Blur Direction

scholarly journals BLIND DECONVOLUTION ON UNDERWATER IMAGES FOR GAS BUBBLE MEASUREMENT

2015 ◽  
Vol XL-5/W5 ◽  
pp. 239-244 ◽  
Author(s):  
C. Zelenka ◽  
R. Koch
Keyword(s):  
Blind Deconvolution ◽  
Motion Blur ◽  
Depth Of Field ◽  
Light Power ◽  
Measurement Systems ◽  
Small Depth ◽  
Open Sea ◽  
Deconvolution Techniques ◽  
Bubble Movement ◽  
Bubble Measurement

Marine gas seeps, such as in the Panarea area near Sicily (McGinnis et al., 2011), emit large amounts of methane and carbon-dioxide, greenhouse gases. Better understanding their impact on the climate and the marine environment requires precise measurements of the gas flux. Camera based bubble measurement systems suffer from defocus blur caused by a combination of small depth of field, insufficient lighting and from motion blur due to rapid bubble movement. These adverse conditions are typical for open sea underwater bubble images. As a consequence so called ’bubble boxes’ have been built, which use elaborate setups, specialized cameras and high power illumination. A typical value of light power used is 1000W (Leifer et al., 2003). <br><br> In this paper we propose the compensation of defocus and motion blur in underwater images by using blind deconvolution techniques. The quality of the images can be greatly improved, which will relax requirements on bubble boxes, reduce their energy consumption and widen their usability.

scholarly journals Using a Blur Metric to Estimate Linear Motion Blur Parameters

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Taiebeh Askari Javaran ◽  
Hamid Hassanpour
Keyword(s):  
Image Processing ◽  
Health Services ◽  
Motion Blur ◽  
Linear Motion ◽  
Sensitive Stage ◽  
Blurred Image ◽  
The Given ◽  
Blur Direction ◽  
Sharp Version ◽  
Monotonic Relation

Motion blur is a common artifact in image processing, specifically in e-health services, which is caused by the motion of a camera or scene. In linear motion cases, the blur kernel, i.e., the function that simulates the linear motion blur process, depends on the length and direction of blur, called linear motion blur parameters. The estimation of blur parameters is a vital and sensitive stage in the process of reconstructing a sharp version of a motion blurred image, i.e., image deblurring. The estimation of blur parameters can also be used in e-health services. Since medical images may be blurry, this method can be used to estimate the blur parameters and then take an action to enhance the image. In this paper, some methods are proposed for estimating the linear motion blur parameters based on the extraction of features from the given single blurred image. The motion blur direction is estimated using the Radon transform of the spectrum of the blurred image. To estimate the motion blur length, the relation between a blur metric, called NIDCT (Noise-Immune Discrete Cosine Transform-based), and the motion blur length is applied. Experiments performed in this study showed that the NIDCT blur metric and the blur length have a monotonic relation. Indeed, an increase in blur length leads to increase in the blurriness value estimated via the NIDCT blur metric. This relation is applied to estimate the motion blur. The efficiency of the proposed method is demonstrated by performing some quantitative and qualitative experiments.

scholarly journals Blind Image Deconvolution Algorithm Based on Sparse Optimization with an Adaptive Blur Kernel Estimation

2020 ◽  
Vol 10 (7) ◽  
pp. 2437 ◽  
Author(s):  
Haoyuan Yang ◽  
Xiuqin Su ◽  
Songmao Chen
Keyword(s):  
Blind Deconvolution ◽  
Imaging System ◽  
Real Life ◽  
Kernel Estimation ◽  
Input Image ◽  
Motion Blur ◽  
Sparse Optimization ◽  
Image Deconvolution ◽  
Deconvolution Algorithm ◽  
Blur Kernel

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 based on nonzero measurement in the image gradient domain and introduces an analytical solution, which converges quickly without additional searching iterations during the optimization. First, a blur kernel is accurately estimated from a single input image with an alternating scheme and a half-quadratic optimization algorithm. Subsequently, the latent sharp image is revealed by a non-blind deconvolution algorithm with the hyper-Laplacian distribution-based priors. Additionally, we analyze and discuss its solutions for different prior parameters. According to the tests we conducted, our method outperforms similar methods and could be suitable for dealing with image blurs in real-life applications.

PSF Estimation and Image Restoration for Motion Blurred Images

2012 ◽  
Vol 562-564 ◽  
pp. 2124-2127 ◽  
Author(s):  
Qi Shen Li ◽  
Jian Gong Chen
Keyword(s):  
Fourier Transform ◽  
Image Restoration ◽  
Signal To Noise Ratio ◽  
Motion Blur ◽  
Blurred Image ◽  
The Fourier Transform ◽  
Original Motion ◽  
Blurred Images ◽  
Blur Direction ◽  
Psf Estimation

Point spread function (PSF) estimation and image restoration algorithm are the hotspots In the research of motion blurred image restoration. In order to improve the efficacy of image restoration, an improved algorithm named quadric transforms (QT) method is proposed in this paper by analyzing the restoration process of motion blurred images. Firstly, Fourier transform and homomorphism transform are applied to the original motion blurred image, and then the Fourier transform and homomorphism transform are used again to the obtained spectrum image. Secondly, the motion blur direction is estimated by Radon transform. Thirdly, the motion blur length is found by differential autocorrelation operations. Finally, utilizing the estimated blur direction and blur length, the motion blurred image is restored by Wiener filtering. Experimental results show that the proposed QT method can get more accurate estimated motion blur angles than the primary transform (PT, that is, Fourier transform and homomorphism transform are used one time) method and can get better restored images under the meaning of peak signal to noise ratio (PSNR).

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