scholarly journals Blind Fusion of Hyperspectral Multispectral Images Based on Matrix Factorization

2021 ◽  
Vol 13 (21) ◽  
pp. 4219
Author(s):  
Jian Long ◽  
Yuanxi Peng
Keyword(s):  
Response Function ◽  
Spatial Resolution ◽  
Point Spread Function ◽  
Spectral Response ◽  
Estimation Method ◽  
Hyperspectral Images ◽  
Fusion Method ◽  
Multispectral Images ◽  
Point Spread ◽  
Spread Function

The fusion of low spatial resolution hyperspectral images and high spatial resolution multispectral images in the same scenario is important for the super-resolution of hyperspectral images. The spectral response function (SRF) and the point spread function (PSF) are two crucial prior pieces of information in fusion, and most of the current algorithms need to provide these two preliminary pieces of information in advance, even for semi-blind fusion algorithms at least the SRF. This causes limitations in the application of fusion algorithms. This paper aims to solve the dependence of the fusion method on the point spread function and proposes a method to estimate the spectral response function from the images involved in the fusion to achieve blind fusion. We conducted experiments on simulated datasets Pavia University, CAVE, and the remote sensing images acquired by two spectral cameras, Sentinel 2 and Hyperion. The experimental results show that our proposed SRF estimation method can improve the PSNR value by 5 dB on average compared with other state-of-the-art SRF estimation results. The proposed blind fusion method can improve the PSNR value of fusion results by 3–15 dB compared with other blind fusion methods.

Simulation of Spectral Response Function based on Huygens Point Spread Function

2015 ◽  
Vol 44 (10) ◽  
pp. 1030002
Author(s):  
高震宇 GAO Zhen-yu ◽  
方伟 FANG Wei ◽  
宋宝奇 SONG Bao-qi ◽  
姜明 JIANG Ming ◽  
王玉鹏 WANG Yu-peng
Keyword(s):  
Response Function ◽  
Point Spread Function ◽  
Spectral Response ◽  
Point Spread ◽  
Spread Function

scholarly journals Simulation and Analysis of Spectral Response Function and Bandwidth of Spectrometer

2016 ◽  
Vol 2016 ◽  
pp. 1-8
Author(s):  
Zhenyu Gao ◽  
Ruidong Jia ◽  
Hao Zhang ◽  
Zhiwei Xia ◽  
Wei Fang
Keyword(s):  
Response Function ◽  
Spectral Response ◽  
Simulation Method ◽  
Design Phase ◽  
Optical Aberrations ◽  
Simulation Procedure ◽  
Point Spread ◽  
Spread Function ◽  
Prism Monochromator ◽  
Tracing Method

A simulation method for acquiring spectrometer’s Spectral Response Function (SRF) based on Huygens Point Spread Function (PSF) is suggested. Taking into account the effects of optical aberrations and diffraction, the method can obtain the fine SRF curve and corresponding spectral bandwidth at any nominal wavelength as early as in the design phase. A prism monochromator is proposed for illustrating the simulation procedure. For comparison, a geometrical ray-tracing method is also provided, with bandwidth deviations varying from 5% at 250 nm to 25% at 2400 nm. Further comparison with reported experiments shows that the areas of the SRF profiles agree to about 1%. However, the weak scattered background light on the level of 10−4 to 10−5 observed by experiment could not be covered by this simulation. This simulation method is a useful tool for forecasting the performance of an underdesigned spectrometer.

scholarly journals Reconstructed spatial resolution and contrast recovery with Bayesian penalized likelihood reconstruction (Q.Clear) for FDG-PET compared to time-of-flight (TOF) with point spread function (PSF)

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Julian M. Rogasch ◽  
Said Suleiman ◽  
Frank Hofheinz ◽  
Stephanie Bluemel ◽  
Mathias Lukas ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Point Spread Function ◽  
Activity Concentration ◽  
Signal To Noise Ratio ◽  
Time Of Flight ◽  
Penalized Likelihood ◽  
Systematic Evaluation ◽  
Point Spread ◽  
Spread Function ◽  
Relative Differences

Abstract Background Bayesian penalized likelihood reconstruction for PET (e.g., GE Q.Clear) aims at improving convergence of lesion activity while ensuring sufficient signal-to-noise ratio (SNR). This study evaluated reconstructed spatial resolution, maximum/peak contrast recovery (CRmax/CRpeak) and SNR of Q.Clear compared to time-of-flight (TOF) OSEM with and without point spread function (PSF) modeling. Methods The NEMA IEC Body phantom was scanned five times (3 min scan duration, 30 min between scans, background, 1.5–3.9 kBq/ml F18) with a GE Discovery MI PET/CT (3-ring detector) with spheres filled with 8-, 4-, or 2-fold the background activity concentration (SBR 8:1, 4:1, 2:1). Reconstruction included Q.Clear (beta, 150/300/450), “PSF+TOF4/16” (iterations, 4; subsets, 16; in-plane filter, 2.0 mm), “OSEM+TOF4/16” (identical parameters), “PSF+TOF2/17” (2 it, 17 ss, 2.0 mm filter), “OSEM+TOF2/17” (identical), “PSF+TOF4/8” (4 it, 8 ss, 6.4 mm), and “OSEM+TOF2/8” (2 it, 8 ss, 6.4 mm). Spatial resolution was derived from 3D sphere activity profiles. RC as (sphere activity concentration [AC]/true AC). SNR as (background mean AC/background AC standard deviation). Results Spatial resolution of Q.Clear150 was significantly better than all conventional algorithms at SBR 8:1 and 4:1 (Wilcoxon, each p < 0.05). At SBR 4:1 and 2:1, the spatial resolution of Q.Clear300/450 was similar or inferior to PSF+TOF4/16 and OSEM+TOF4/16. Small sphere CRpeak generally underestimated true AC, and it was similar for Q.Clear150/300/450 as with PSF+TOF4/16 or PSF+TOF2/17 (i.e., relative differences < 10%). Q.Clear provided similar or higher CRpeak as OSEM+TOF4/16 and OSEM+TOF2/17 resulting in a consistently better tradeoff between CRpeak and SNR with Q.Clear. Compared to PSF+TOF4/8/OSEM+TOF2/8, Q.Clear150/300/450 showed lower SNR but higher CRpeak. Conclusions Q.Clear consistently improved reconstructed spatial resolution at high and medium SBR compared to PSF+TOF and OSEM+TOF, but only with beta = 150. However, this is at the cost of inferior SNR with Q.Clear150 compared to Q.Clear300/450 and PSF+TOF4/16/PSF+TOF2/17 while CRpeak for the small spheres did not improve considerably. This suggests that Q.Clear300/450 may be advantageous for the 3-ring detector configuration because the tradeoff between CR and SNR with Q.Clear300/450 was superior to PSF+TOF4/16, OSEM+TOF4/16, and OSEM+TOF2/17. However, it requires validation by systematic evaluation in patients at different activity and acquisition protocols.

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