P‐79: Evaluation of Diffraction Induced Background Image Quality Degradation through Transparent OLED Display

2019 ◽  
Vol 50 (1) ◽  
pp. 1533-1536
Author(s):  
Chi Jui Cheng ◽  
Tzu Chin Huang ◽  
Wen Tsan Lin ◽  
Cheng Chih Hsieh ◽  
Peng Yu Chen ◽  
...  
2019 ◽  
Vol 2019 (1) ◽  
pp. 104-107
Author(s):  
Ye Seul Baek ◽  
Youngshin Kwak ◽  
Sehyeok Park

The image quality is affected by the black luminance level of the image. This research aimed to investigate how low luminance levels are required to maintain image quality. The psychophysical experiment was carried out in a dark room using OLED display. Total of 6 different black luminance levels (0.003, 0.05, 0.1, 0.2, 0.3, and 1 cd/m2) were used in the experiment. Total of 20 participants was invited to evaluate the image quality. For the experiment, twelve test images are used and these test images categorized into three groups as dark, medium bright and bright image group by image histogram distribution. Each image is rendered by adjusting six different black luminance levels. Result found that the black level is higher than 0.1 cd/m2, the preference for the image is decreased. The best performance is achieved when the black level is 0.003 cd/m2, but there is no big difference from 0.1 cd/m2. The final result shows that a change in black level between about 0.003 cd/m2 and 0.1 cd/m2 does not significantly affect image quality.


2021 ◽  
Vol 52 (S1) ◽  
pp. 643-646
Author(s):  
Yang Guobo ◽  
Qiu Haijun ◽  
Huang Weiyun ◽  
Yang Yuqing ◽  
Long Yue ◽  
...  

2019 ◽  
Author(s):  
Sabrina Asteriti ◽  
Valeria Ricci ◽  
Lorenzo Cangiano

ABSTRACTTissue clearing techniques are undergoing a renaissance motivated by the need to image fluorescence deep in biological samples without physical sectioning. Optical transparency is achieved by equilibrating tissues with high refractive index (RI) solutions, which require expensive optimized objectives to avoid aberrations. One may thus need to assess whether an available objective is suitable for a specific clearing solution, or the impact on imaging of small mismatches between cleared sample and objective design RIs. We derived closed form approximations for image quality degradation versus RI mismatch and other parameters available to the microscopist. We validated them with computed (and experimentally confirmed) aberrated point spread functions, and by imaging fluorescent neurons in high RI solutions. Crucially, we propose two simple numerical criteria to establish: (i) the degradation in image quality (brightness and resolution) from optimal conditions of any clearing solution/objective combination; (ii) which objective, among several, achieves the highest resolution in a given immersion medium. These criteria apply directly to the widefield fluorescent microscope but are also closely relevant to more advanced microscopes.


2018 ◽  
Vol 57 (11) ◽  
pp. 2851 ◽  
Author(s):  
Jueqin Qiu ◽  
Haisong Xu ◽  
Zhengnan Ye ◽  
Changyu Diao

2012 ◽  
Vol 39 (6Part8) ◽  
pp. 3682-3682
Author(s):  
A Negri ◽  
D Michelutti ◽  
R Padovani ◽  
E Moretti

2021 ◽  
Vol 17 (11) ◽  
pp. 2265-2270
Author(s):  
Jiajie Wang ◽  
Junmei Zeng

The texture complexity of traditional sensor image degradation restoration methods is high and the restoration effect is reduced. For this reason, a virtual reality-based image quality degradation recovery method for nanosensors is designed in this paper. First, the image quality degradation model of nanometer sensor is constructed based on virtual reality technology. Then, the noise characteristics of the degraded image are analyzed. On the premise of retaining the original image information, the diffusion coefficients in the vertical and horizontal directions are calculated to obtain the expression of adaptive filter (ADF) in the image with noise, so as to complete the image denoising process. On the basis of texture complexity analysis, singular value decomposition detection and alpha channel calculation are completed, and image quality degradation recovery of nanosensor is achieved through synthesis operation. The experimental results show that the texture complexity of the recovered images is lower than 0.54, the average absolute error percentage of the recovered images is only 10%, and the P-R value is high, which fully demonstrates the effectiveness of the offered procedure.


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