Application of multi-frame approach in single-frame blind deconvolution

2012 ◽  
Vol 285 (24) ◽  
pp. 4937-4940 ◽  
Author(s):  
Dongfeng Shi ◽  
Chengyu Fan ◽  
Hong Shen ◽  
Pengfei Zhang ◽  
Jinghui Zhang ◽  
...  
Keyword(s):  
Blind Deconvolution ◽  
Single Frame

Single-frame blind deconvolution by means of frame segmentation

1994 ◽  
Vol 19 (10) ◽  
pp. 695 ◽  
Author(s):  
Noriaki Miura ◽  
Kenro Ohsawa ◽  
Naoshi Baba
Keyword(s):  
Blind Deconvolution ◽  
Single Frame

SINGLE FRAME VIDEO FOR LANGUAGE INSTRUCTION

2013 ◽  
Vol 1 (2) ◽  
pp. 7-9
Author(s):  
Michael DeBloois
Keyword(s):  
Language Instruction ◽  
Single Frame

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

Is Rotoscope True Animation?

2018 ◽  
Vol 10 (1) ◽  
pp. 12-18
Author(s):  
Christine Mersiana Lukmanto
Keyword(s):  
Single Frame ◽  
The Aesthetic ◽  
Live Action

Throughout the years, technologies have developed varied techniques for animators to create films. One of the oldest and interesting techniques of animating is Rotoscope by tracing every single frame from live action footages and enhancing it with animator’s style. In terms of anthropomorphism, it creates an uncanny realistic effect because it mimics the reality of the structure, the proportion and the movement. Due to its easy process in animating, there are some critiques and debates about how far rotoscoping can be considered as the “true” animation. This research would cover the identity of rotoscoping, professionals and animator’s perspective as well as the aesthetic of this technique. Keywords: rotoscope, realism, true animation, perspective and aesthetic

Blind Deconvolution to Improve Classification of Transient Source Signals in Multipath

2000 ◽  
Author(s):  
Lisa A. Pflug ◽  
George B. Smith ◽  
Michael K. Broadhead
Keyword(s):  
Blind Deconvolution ◽  
Transient Source ◽  
Source Signals
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