Motion blur analysis based on image segmentation and blind deconvolution

2010 ◽  
Author(s):  
Chao Xing ◽  
Yanjun Li ◽  
Ke Zhang
Keyword(s):  
Image Segmentation ◽  
Blind Deconvolution ◽  
Motion Blur

scholarly journals Quantification of Blood Flow Velocity in the Human Conjunctival Microvessels Using Deep Learning-Based Stabilization Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3224
Author(s):  
Hang-Chan Jo ◽  
Hyeonwoo Jeong ◽  
Junhyuk Lee ◽  
Kyung-Sun Na ◽  
Dae-Yu Kim
Keyword(s):  
Blood Flow ◽  
Image Segmentation ◽  
Deep Learning ◽  
Flow Velocity ◽  
Blood Flow Velocity ◽  
Template Matching ◽  
Motion Correction ◽  
Motion Blur ◽  
Motion Artifacts ◽  
Human Conjunctiva

The quantification of blood flow velocity in the human conjunctiva is clinically essential for assessing microvascular hemodynamics. Since the conjunctival microvessel is imaged in several seconds, eye motion during image acquisition causes motion artifacts limiting the accuracy of image segmentation performance and measurement of the blood flow velocity. In this paper, we introduce a novel customized optical imaging system for human conjunctiva with deep learning-based segmentation and motion correction. The image segmentation process is performed by the Attention-UNet structure to achieve high-performance segmentation results in conjunctiva images with motion blur. Motion correction processes with two steps—registration and template matching—are used to correct for large displacements and fine movements. The image displacement values decrease to 4–7 μm during registration (first step) and less than 1 μm during template matching (second step). With the corrected images, the blood flow velocity is calculated for selected vessels considering temporal signal variances and vessel lengths. These methods for resolving motion artifacts contribute insights into studies quantifying the hemodynamics of the conjunctiva, as well as other tissues.

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 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.

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