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Showing papers on "Parametric Image published in 2021"


Journal ArticleDOI
TL;DR: An SAR parametric image reconstruction method (SPIRM) is proposed that establishes a parametric framework to recover SLEs from SAR echoes and reveals the most essential difference between the residual endpoints of a disappeared SLE and points.
Abstract: The edges of a target provide essential geometric information and are extremely important for human visual perception and image recognition. However, due to the coherent superposition of received echoes, the continuous edges of targets are discretized in synthetic aperture radar (SAR) images, i.e., the edges become dispersed points, which seriously affects the extraction of visual and geometric information from SAR images. In this article, we focus on solving the problem of how to recover smooth linear edges (SLEs). By introducing multiangle observations, we propose an SAR parametric image reconstruction method (SPIRM) that establishes a parametric framework to recover SLEs from SAR echoes. At the core of the SPIRM is a novel physical characteristic parameter called the scattering-phase-mutation feature (SPMF), which reveals the most essential difference between the residual endpoints of a disappeared SLE and points. Numerical simulations and real-data experiments demonstrate the robustness and effectiveness of the proposed method.

15 citations


Journal ArticleDOI
TL;DR: In this paper, a spectral analysis based model for dynamic reconstruction and parametric imaging of PatlakKi was proposed and evaluated for DWB FDG protocols and compared against 3D reconstruction based parametric images from SB dynamic protocols.
Abstract: Dynamic whole body (DWB) PET acquisition protocols enable the use of whole body parametric imaging for clinical applications. In FDG imaging, accurate parametric images of PatlakKican be complementary to regular standardised uptake value images and improve on current applications or enable new ones. In this study we consider DWB protocols implemented on clinical scanners with a limited axial field of view with the use of multiple whole body sweeps. These protocols result in temporal gaps in the dynamic data which produce noisier and potentially more biased parametric images, compared to single bed (SB) dynamic protocols. Dynamic reconstruction using the Patlak model has been previously proposed to overcome these limits and shown improved DWB parametric images ofKi. In this work, we propose and make use of a spectral analysis based model for dynamic reconstruction and parametric imaging of PatlakKi. Both dynamic reconstruction methods were evaluated for DWB FDG protocols and compared against 3D reconstruction based parametric imaging from SB dynamic protocols. This work was conducted on simulated data and results were tested against real FDG dynamic data. We showed that dynamic reconstruction can achieve levels of parametric image noise and bias comparable to 3D reconstruction in SB dynamic studies, with the spectral model offering additional flexibility and further reduction of image noise. Comparisons were also made between step and shoot and continuous bed motion (CBM) protocols, which showed that CBM can achieve lower parametric image noise due to reduced acquisition temporal gaps. Finally, our results showed that dynamic reconstruction improved VOI parametric mean estimates but did not result to fully converged values before resulting in undesirable levels of noise. Additional regularisation methods need to be considered for DWB protocols to ensure both accurate quantification and acceptable noise levels for clinical applications.

6 citations


Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper proposed a temporal non-local convolutional neural network to estimate the motion corrected full-dose direct Patlak images from the dynamic PET reconstruction series.

4 citations


Proceedings ArticleDOI
12 Apr 2021
TL;DR: The proposed parametric image modelling approach provides sensor system designers with increased confidence in their design and compliance, and this helps reduces the early design risk.
Abstract: The accuracy of modelled performance data for EO/IR sensors is often limited by the accuracy with which image processing functions can be represented in system-level models and simulations. This is particularly so for those cases where complex processing functions are required, such as those found in autonomous ATD/R systems. Furthermore, for sensors mounted on moving platforms, variability in the frame-to-frame image quality can dominate the achieved measures of performance and effectiveness during an engagement. An established technique to address this involves the use of image-based simulations which process dynamically changing imagery using representative image processing functions. However, such an approach requires extensive run-times and a large volume of real or synthetic image data, both of which can be prohibitive. An alternative approach is presented here whereby a limited number of images are processed and then used to generate statistically based performance transfer functions using an appropriate interpolation scheme. These transfer functions are then used to represent the output response of the processing chain when the received imagery is subjected to different levels of degradations such as distortion and blurring. Such transfer functions can then be stored in multidimensional look-up tables which can be rapidly accessed by a system-level Monte-Carlo performance simulation. The ability to represent and extract the performance-related transfer functions is dependent upon the image quality metrics and the accuracy of the corresponding parametric model requires careful consideration of the model validation. An example simulation is presented based on an autonomous ATD/R sensor system mounted on an airborne platform. The importance of validation is demonstrated, and the increased run-time benefits are described. The proposed parametric image modelling approach provides sensor system designers with increased confidence in their design and compliance, and this helps reduces the early design risk.

3 citations


Journal ArticleDOI
TL;DR: In this paper, the authors used guided kernel means (GKM) and dynamic PET image information to conduct guided filtering and perform parametric image reconstruction and applied this method to direct and indirect reconstruction, and through computer simulations, they showed that their proposed method has higher identifiability and a greater signal-to-noise ratio (SNR) than conventional direct or indirect reconstruction methods.
Abstract: The method of reconstructing parametric images from dynamic positron emission tomography (PET) data with the linear Patlak model has been widely used in scientific research and clinical practice. Whether for direct or indirect image reconstruction, researchers have deeply investigated the associated methods and effects. Among the existing methods, the traditional maximum likelihood expectation maximization (MLEM) reconstruction algorithm is fast but produces a substantial amount of noise. If the parameter images obtained by the MLEM algorithm are postfiltered, a large amount of image edge information is lost. Additionally, although the kernel method has a better noise reduction effect, its calculation costs are very high due to the complexity of the algorithm. Therefore, to obtain parametric images with a high signal-to-noise ratio (SNR) and good retention of detailed information, here, we use guided kernel means (GKM) and dynamic PET image information to conduct guided filtering and perform parametric image reconstruction. We apply this method to direct and indirect reconstruction, and through computer simulations, we show that our proposed method has higher identifiability and a greater SNR than conventional direct and indirect reconstruction methods. We also show that our method produces better images with direct than with indirect reconstruction.

1 citations



Posted Content
TL;DR: Zhang et al. as discussed by the authors proposed a novel system called functional neural network (FuncNet) to solve a parametric image restoration problem with a single model, where the smallest conceptual element of a neural network is no longer a floating-point variable, but a function of the parameter of the problem.
Abstract: Almost every single image restoration problem has a closely related parameter, such as the scale factor in super-resolution, the noise level in image denoising, and the quality factor in JPEG deblocking. Although recent studies on image restoration problems have achieved great success due to the development of deep neural networks, they handle the parameter involved in an unsophisticated way. Most previous researchers either treat problems with different parameter levels as independent tasks, and train a specific model for each parameter level; or simply ignore the parameter, and train a single model for all parameter levels. The two popular approaches have their own shortcomings. The former is inefficient in computing and the latter is ineffective in performance. In this work, we propose a novel system called functional neural network (FuncNet) to solve a parametric image restoration problem with a single model. Unlike a plain neural network, the smallest conceptual element of our FuncNet is no longer a floating-point variable, but a function of the parameter of the problem. This feature makes it both efficient and effective for a parametric problem. We apply FuncNet to super-resolution, image denoising, and JPEG deblocking. The experimental results show the superiority of our FuncNet on all three parametric image restoration tasks over the state of the arts.