Study of advanced mask inspection optics with super-resolution method for next-generation mask fabrication

2009 ◽  
Author(s):  
Ryoichi Hirano ◽  
Masatoshi Hirono ◽  
Riki Ogawa ◽  
Nobutaka Kikuiri ◽  
Kenichi Takahara ◽  
...  
Keyword(s):  
Super Resolution ◽  
Next Generation ◽  
Resolution Method ◽  
Mask Inspection ◽  
Mask Fabrication

scholarly journals Implementasi Metode Super Resolusi Untuk Meningkatkan Kualitas Citra Hasil Screenshot

2020 ◽  
Vol 7 (3) ◽  
pp. 432
Author(s):  
Windi Astuti
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Median Filter ◽  
Super Resolution ◽  
Linear Interpolation ◽  
Resolution Method ◽  
Image Zooming ◽  
Image Enlargement ◽  
Three Stages

Various types of image processing that can be done by computers, such as improving image quality is one of the fields that is quite popular until now. Improving the quality of an image is necessary so that someone can observe the image clearly and in detail without any disturbance. An image can experience major disturbances or errors in an image such as the image of the screenshot is used as a sample. The results of the image from the screenshot have the smallest sharpness and smoothness of the image, so to get a better image is usually done enlargement of the image. After the screenshot results are obtained then, the next process is cropping the image and the image looks like there are disturbances such as visible blur and cracked. To get an enlarged image (Zooming image) by adding new pixels or points. This is done by the super resolution method, super resolution has three stages of completion, first Registration, Interpolation, and Reconstruction. For magnification done by linear interpolation and reconstruction using a median filter for image refinement. This method is expected to be able to solve the problem of improving image quality in image enlargement applications. This study discusses that the process carried out to implement image enlargement based on the super resolution method is then built by using R2013a matlab as an editor to edit programs

scholarly journals A Novel Bayesian Super-Resolution Method for Radar Forward-Looking Imaging Based on Markov Random Field Model

2021 ◽  
Vol 13 (20) ◽  
pp. 4115
Author(s):  
Ke Tan ◽  
Xingyu Lu ◽  
Jianchao Yang ◽  
Weimin Su ◽  
Hong Gu
Keyword(s):  
Random Field ◽  
Objective Function ◽  
Markov Random Field ◽  
Prior Information ◽  
Structural Characteristics ◽  
Super Resolution ◽  
Resolution Method ◽  
Markov Random ◽  
Shrinkage Method ◽  
Forward Looking

Super-resolution technology is considered as an efficient approach to promote the image quality of forward-looking imaging radar. However, super-resolution technology is inherently an ill-conditioned issue, whose solution is quite susceptible to noise. Bayesian method can efficiently alleviate this issue through utilizing prior knowledge of the imaging process, in which the scene prior information plays a pretty significant role in ensuring the imaging accuracy. In this paper, we proposed a novel Bayesian super-resolution method on the basis of Markov random field (MRF) model. Compared with the traditional super-resolution method which is focused on one-dimensional (1-D) echo processing, the MRF model adopted in this study strives to exploit the two-dimensional (2-D) prior information of the scene. By using the MRF model, the 2-D spatial structural characteristics of the imaging scene can be well described and utilized by the nth-order neighborhood system. Then, the imaging objective function can be constructed through the maximum a posterior (MAP) framework. Finally, an accelerated iterative threshold/shrinkage method is utilized to cope with the objective function. Validation experiments using both synthetic echo and measured data are designed, and results demonstrate that the new MAP-MRF method exceeds other benchmarking approaches in terms of artifacts suppression and contour recovery.

scholarly journals Next-generation biomarkers based on 100-parameter functional super-resolution microscopy TIS

2012 ◽  
Vol 29 (5) ◽  
pp. 599-610 ◽  
Author(s):  
Walter Schubert ◽  
Anne Gieseler ◽  
Andreas Krusche ◽  
Peter Serocka ◽  
Reyk Hillert
Keyword(s):  
Super Resolution ◽  
Next Generation ◽  
Super Resolution Microscopy

SRKTDN: Applying Super Resolution Method to Dehazing Task

2021 ◽  
Author(s):  
Tianyi Chen ◽  
Jiahui Fu ◽  
Wentao Jiang ◽  
Chen Gao ◽  
Si Liu
Keyword(s):  
Super Resolution ◽  
Resolution Method
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