SU-E-J-130: Study of the Image Quality Degradation in Phase-Based 4DCT Imaging for Radiation Oncology

2012 ◽  
Vol 39 (6Part8) ◽  
pp. 3682-3682
Author(s):  
A Negri ◽  
D Michelutti ◽  
R Padovani ◽  
E Moretti
Keyword(s):  
Image Quality ◽  
Radiation Oncology ◽  
Quality Degradation

scholarly journals Two simple criteria to estimate an objective’s performance when imaging in non design tissue clearing solutions

2019 ◽  
Author(s):  
Sabrina Asteriti ◽  
Valeria Ricci ◽  
Lorenzo Cangiano
Keyword(s):  
Image Quality ◽  
High Refractive Index ◽  
Optimal Conditions ◽  
Point Spread Functions ◽  
Tissue Clearing ◽  
Fluorescent Microscope ◽  
Immersion Medium ◽  
Quality Degradation ◽  
Point Spread ◽  
The Impact

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.

Image quality degradation of object-color metamer mismatching in digital camera color reproduction

2018 ◽  
Vol 57 (11) ◽  
pp. 2851 ◽  
Author(s):  
Jueqin Qiu ◽  
Haisong Xu ◽  
Zhengnan Ye ◽  
Changyu Diao
Keyword(s):  
Image Quality ◽  
Digital Camera ◽  
Color Reproduction ◽  
Quality Degradation

Restoration Simulation of Image Quality Degradation of Nanosensors Based on Virtual Reality

2021 ◽  
Vol 17 (11) ◽  
pp. 2265-2270
Author(s):  
Jiajie Wang ◽  
Junmei Zeng
Keyword(s):  
Virtual Reality ◽  
Image Quality ◽  
Complexity Analysis ◽  
Absolute Error ◽  
Channel Calculation ◽  
Recovery Method ◽  
Noise Characteristics ◽  
Quality Degradation ◽  
Texture Complexity ◽  
Value Decomposition

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.

scholarly journals Image-quality degradation and retrieval errors introduced by registration and interpolation of multispectral digital images

1996 ◽  
Author(s):  
Bradley G. Henderson ◽  
Christoph C. Borel ◽  
James P. Theiler ◽  
Barham W. Smith
Keyword(s):  
Image Quality ◽  
Digital Images ◽  
Quality Degradation ◽  
Retrieval Errors
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