Partially Overlapping Printing to Improve Image Quality of Volume Hologram Printer – Numerical Simulation –

2019 ◽  
Author(s):  
Hangbo Hua ◽  
Takeshi Yamaguchi ◽  
Hiroshi Yoshikawa
Keyword(s):  
Numerical Simulation ◽  
Image Quality ◽  
Improve Image Quality ◽  
Volume Hologram

scholarly journals Partially Overlapping Printing with Digital Filter to Improve Quality of Volume Hologram Printer – Numerical Simulation –

2020 ◽  
Vol 10 (11) ◽  
pp. 3963
Author(s):  
Hangbo Hua ◽  
Takeshi Yamaguchi ◽  
Hiroshi Yoshikawa
Keyword(s):  
Numerical Simulation ◽  
Image Quality ◽  
Spatial Filter ◽  
Improve Quality ◽  
3D Display ◽  
Improve Image Quality ◽  
Volume Hologram ◽  
Final Volume ◽  
Printing Speed

The volume hologram printer is useful for 3D display, because it is selective to the wavelength and be able to reconstruct with the natural illumination. There are many studies of a volume hologram printer been studied to output a volume hologram from a computer-generated hologram. The final volume hologram consists of tiled small holograms and the tiling manner often causes spilt lines which will have impact on image quality. With an intent to get rid of the split lines and improve the quality, fully overlapping printing was proposed recently. Each elemental hologram is overlapped both in vertical and horizontal directions by 50%. Then, the hologram is printed four times in each area and it makes the printing speed four times slower. For this case, partially overlapping printing is proposed in this paper to improve image quality with small effect in printing speed. For partial overlapping, a digital spatial filter is projected and added to every elemental hologram. Using the digital spatial filter, different partially overlapped holograms are calculated and reconstructed to compare to the non-overlapped ones. The simulation result shows that the overlapped one (10% in both vertical and horizontal) has much weaker gaps and black lines.

P-47: New Signal Processing Method to Improve Image Quality of RGBW Display

2014 ◽  
Vol 45 (1) ◽  
pp. 1149-1152
Author(s):  
Masaaki Kabe ◽  
Toshiyuki Nagatsuma ◽  
Amane Higashi ◽  
Tae Nakahara ◽  
Kojiro Ikeda ◽  
...  
Keyword(s):  
Signal Processing ◽  
Image Quality ◽  
Processing Method ◽  
Improve Image Quality ◽  
Signal Processing Method

Erratum: ``Double-layer'' method to improve image quality of industria SPECT

2012 ◽  
Vol 7 (01) ◽  
pp. E01001-E01001
Author(s):  
J G Park ◽  
H Seo ◽  
C H Kim ◽  
S H Jung ◽  
J B Kim ◽  
...  
Keyword(s):  
Image Quality ◽  
Double Layer ◽  
Improve Image Quality ◽  
Layer Method

scholarly journals Individual transducer impulse response characterization method to improve image quality of array-based handheld optoacoustic tomography

2020 ◽  
Vol 46 (1) ◽  
pp. 1
Author(s):  
Kaushik Basak Chowdhury ◽  
Maximilian Bader ◽  
Christoph Dehner ◽  
Dominik Jüstel ◽  
Vasilis Ntziachristos
Keyword(s):  
Image Quality ◽  
Impulse Response ◽  
Improve Image Quality ◽  
Optoacoustic Tomography

scholarly journals ANALISIS PERBANDINGAN METODE FILTER MEAN, MEDIAN, MAXIMUM, MINIMUM, DAN GAUSSIAN TERHADAP REDUKSI NOISE GAUSSIAN, SALT&PAPPER , SPECKLE, POISSON, DAN LOCALVAR

2019 ◽  
Vol 17 (1) ◽  
pp. 15
Author(s):  
I Gede Aris Gunadi
Keyword(s):  
Image Quality ◽  
Image Restoration ◽  
Median Filter ◽  
Improve Image Quality ◽  
Mean Filter ◽  
The Right ◽  
Influence Of Noise ◽  
Median Maximum ◽  
Maximum Minimum

Due to the influence of noise on an image, the image will experience a decrease in quality.  If the type of noise is known for certain, then the right solution can be determined to restore the condition of an image so that the condition returns to normal. The effort to restore the image condition is stated by image restoration. The most important thing in image restoration is determining the type of noise and the solution for the noise.In this study several types of noise were tried,  gaussian, salt & paper, speckle, poisson, and Localvar on several image samples. In the image that had been exposed to noise, repairs were carried out with several types of filters including gaussian, mean, median, maximum, and minimum. Next was the quality of noise reduction with each filter  determined based on the value of PSNR and MSE. The results of image restoration experiments showed that the mean filter was the best filter used to improve noisegaussian, salt & peppers and speckle image quality. The median filter is the filter that is best used to improve image quality with poisson and localvar noise types.  

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