Optical Image Deblurring Methods

1971 ◽  
pp. 259-308 ◽  
Author(s):  
Jumpei Tsujiuchi ◽  
George W. Stroke
Keyword(s):  
Image Deblurring ◽  
Optical Image

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

VIS-NIR LED Illumination in Backside Circuit Edit and Optical Probing Applications

2012 ◽  
Author(s):  
Shida Tan ◽  
Richard H. Livengood ◽  
Dane Scott ◽  
Roy Hallstein ◽  
Pat Pardy ◽  
...  
Keyword(s):  
High Resolution ◽  
Image Quality ◽  
Optical Imaging ◽  
Narrow Band ◽  
Light Emitting Diode ◽  
Air Gap ◽  
Optical Image ◽  
Proof Of Concept ◽  
Light Emitting ◽  
Optical Probing

Abstract High resolution optical imaging is critical in assisting backside circuit edit (CE) and optical probing navigation. In this paper, we demonstrated improved optical image quality using VIS-NIR narrow band light emitting diode (LED) illumination in various FIB and optical probing platforms. The proof of concept was demonstrated with both common non-contact air gap lenses and solid immersion lenses (SIL).

Sparsity and Smoothing Multi-Regularization Constraints for Blind Image Deblurring

2013 ◽  
Vol 24 (5) ◽  
pp. 1143-1154 ◽  
Author(s):  
Shu TANG ◽  
Wei-Guo GONG ◽  
Jian-Hua ZHONG
Keyword(s):  
Image Deblurring ◽  
Blind Image Deblurring ◽  
Blind Image

METHODS AND ALGORITHMS FOR THE COMPLETE PROCESSING OF A POSTDETECTOR LOW-CONTRAST OPTICAL IMAGE

2018 ◽  
pp. 99-107
Author(s):  
V. V. Lavrov ◽  
R. S. Luchkin ◽  
O. I. Nemykin ◽  
M. E. Prokhorov ◽  
Yu. G. Ryndin ◽  
...  
Keyword(s):  
Optical Image ◽  
Structural Elements ◽  
Filtration Process ◽  
Low Contrast ◽  
Information Object ◽  
Detection Stage ◽  
Metric Structures ◽  
Spot 5 ◽  
Integrated Indicators ◽  
Complete Processing

Methods and algorithms for the complete processing of a post-detector low-contrast optical image (OI) of an unknown remote object obtained by ground-based optical means of observation under conditions of a complex background situation are considered. The purpose of processing is to separate and interpret at least with the help of the analyst, of the main constructive elements using the integrated indicators introduced in [6] and the characteristics of the analyzed OI, which are connected by the information, topological and metric structures of the OI. The stages of processing the OI include extracting the image-containing information object of the image portion (detection) and filtration of the OI, using recursive rank filtering. The final stages of processing include the segmentation of the OI and the allocation on it constructive elements using the apparatus of graph theory. An example of image processing of a Spot-5 spacecraft obtained in real conditions is given. It is shown that in this case at the detection stage it is possible to reduce the volume of information processed at subsequent stages by 8 times, in the filtration process to increase the compactness of the OI and to increase its connectivity in comparison with the post-detection OI. As a result of segmentation and allocation of constructive elements, three structural elements that can be interpreted as a spacecraft case and two remote panels can be identified with the analyst’s participation.

scholarly journals A Lightweight Fusion Distillation Network for Image Deblurring and Deraining

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5312
Author(s):  
Yanni Zhang ◽  
Yiming Liu ◽  
Qiang Li ◽  
Jianzhong Wang ◽  
Miao Qi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Deblurring ◽  
Image Features ◽  
Image Feature ◽  
Model Complexity ◽  
Small Scale ◽  
Feature Maps ◽  
Learning Framework ◽  
Channel Information ◽  
Scale Spaces

Recently, deep learning-based image deblurring and deraining have been well developed. However, most of these methods fail to distill the useful features. What is more, exploiting the detailed image features in a deep learning framework always requires a mass of parameters, which inevitably makes the network suffer from a high computational burden. We propose a lightweight fusion distillation network (LFDN) for image deblurring and deraining to solve the above problems. The proposed LFDN is designed as an encoder–decoder architecture. In the encoding stage, the image feature is reduced to various small-scale spaces for multi-scale information extraction and fusion without much information loss. Then, a feature distillation normalization block is designed at the beginning of the decoding stage, which enables the network to distill and screen valuable channel information of feature maps continuously. Besides, an information fusion strategy between distillation modules and feature channels is also carried out by the attention mechanism. By fusing different information in the proposed approach, our network can achieve state-of-the-art image deblurring and deraining results with a smaller number of parameters and outperform the existing methods in model complexity.

A double gimbal optical image stabilizer driven by piezoelectric stacks

2021 ◽  
Vol 92 (4) ◽  
pp. 045008
Author(s):  
Zhangfan Xu ◽  
Song Pan ◽  
Lei Chen ◽  
Zhong Xiao
Keyword(s):  
Optical Image ◽  
Piezoelectric Stacks
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