Learning to synthesize dense focal stack and all-in-focus image from sparse focal stack

2021 ◽  
Author(s):  
Fumitaka Tateiwa ◽  
Keita Takahashi ◽  
Toshiaki Fujii
Keyword(s):  
Focus Image ◽  
Focal Stack

Search mode for rapid detection of virus particles

1991 ◽  
Vol 49 ◽  
pp. 286-287
Author(s):  
O. E. Bradfute
Keyword(s):  
Electron Microscopy ◽  
Rapid Detection ◽  
Plant Virus ◽  
Low Dose ◽  
Search Mode ◽  
Virus Diseases ◽  
Virus Particles ◽  
Focus Image ◽  
Time Required ◽  
Rapid Switching

Electron microscopy is frequently used in preliminary diagnosis of plant virus diseases by surveying negatively stained preparations of crude extracts of leaf samples. A major limitation of this method is the time required to survey grids when the concentration of virus particles (VPs) is low. A rapid survey of grids for VPs is reported here; the method employs a low magnification, out-of-focus Search Mode similar to that used for low dose electron microscopy of radiation sensitive specimens. A higher magnification, in-focus Confirm Mode is used to photograph or confirm the detection of VPs. Setting up the Search Mode by obtaining an out-of-focus image of the specimen in diffraction (K. H. Downing and W. Chiu, private communications) and pre-aligning the image in Search Mode with the image in Confirm Mode facilitates rapid switching between Modes.

Multi-focus Image Fusion Based on Multi-scale Gradients and Image Matting

2021 ◽  
pp. 1-1
Author(s):  
Jun Chen ◽  
Xuejiao Li ◽  
Linbo Luo ◽  
Jiayi Ma
Keyword(s):  
Image Fusion ◽  
Image Matting ◽  
Multi Scale ◽  
Focus Image

Gradient-based multi-focus image fusion method using convolution neural network

2021 ◽  
Vol 92 ◽  
pp. 107174
Author(s):  
Yang Zhou ◽  
Xiaomin Yang ◽  
Rongzhu Zhang ◽  
Kai Liu ◽  
Marco Anisetti ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Fusion ◽  
Convolution Neural Network ◽  
Fusion Method ◽  
Gradient Based ◽  
Focus Image ◽  
Image Fusion Method

NSCT and focus measure optimization based multi-focus image fusion

2021 ◽  
pp. 1-13
Author(s):  
N. Aishwarya ◽  
C. BennilaThangammal ◽  
N.G. Praveena
Keyword(s):  
Simulation Analysis ◽  
Structural Information ◽  
Research Area ◽  
Processing Technique ◽  
The Novel ◽  
Phase Congruency ◽  
Focus Measure ◽  
Fused Image ◽  
Focus Image ◽  
Image Pairs

Getting a complete description of scene with all the relevant objects in focus is a hot research area in surveillance, medicine and machine vision applications. In this work, transform based fusion method called as NSCT-FMO, is introduced to integrate the image pairs having different focus features. The NSCT-FMO approach basically contains four steps. Initially, the NSCT is applied on the input images to acquire the approximation and detailed structural information. Then, the approximation sub band coefficients are merged by employing the novel Focus Measure Optimization (FMO) approach. Next, the detailed sub-images are combined using Phase Congruency (PC). Finally, an inverse NSCT operation is conducted on synthesized sub images to obtain the initial synthesized image. To optimize the initial fused image, an initial decision map is first constructed and morphological post-processing technique is applied to get the final map. With the help of resultant map, the final synthesized output is produced by the selection of focused pixels from input images. Simulation analysis show that the NSCT-FMO approach achieves fair results as compared to traditional MST based methods both in qualitative and quantitative assessments.

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