Regularization parameter trimming for iterative image reconstruction

Liang, Haoyi; Weller, Daniel S.

doi:10.1109/acssc.2015.7421235

Cited by 7 publications

(10 citation statements)

References 21 publications

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“…In situations where target algorithms converge slowly or the set of parameter candidates is large, assessing image qualities and selecting the best parameter after all the algorithm instances converge would be too time-consuming to be practical. Instead of placing the quality monitor at the output end, a novel parameter trimming framework proposed in [6] integrates the quality monitor into the target algorithms. By doing so, parameters that do not have the potential to achieve good results are trimmed before convergence.…”

Section: Parameter Selectionmentioning

confidence: 99%

“…Parameter selection [6], [7], [41]- [47] is of importance to these target algorithms. By parameter selection, some of these target algorithms [45], [46] achieve a faster convergence rate; some [43], [44] obtain a better restored image.…”

Section: Parameter Selectionmentioning

confidence: 99%

“…Moreover, most image restoration algorithms require one or more parameters to regulate the restoration process, and no-reference IQA methods can be used to guide selecting the parameters. For instance, the regularization parameter of image reconstruction [6] is selected by a no-reference image quality index [7]. However, most existing no-reference IQA algorithms output the estimated image quality based on a single distorted image, ignoring that different degraded images can provide more information together to the quality estimation of each degraded image.…”

Section: Introductionmentioning

confidence: 99%

“…The framework of parameter trimming, first proposed in [6], is designed to boost the parameter selection by combining NR-IQA with parameter selection. In [6], parameters that do not show the potential to obtain the best result are cut during the convergence process.…”

Section: Introductionmentioning

confidence: 99%

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Comparison-Based Image Quality Assessment for Selecting Image Restoration Parameters

Liang¹,

Weller²

2016

IEEE Trans. on Image Process.

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Abstract-Image quality assessment (IQA) is traditionally classified into full-reference (FR) IQA and no-reference (NR) IQA according to whether the original image is required. Although NR-IQA is widely used in practical applications, room for improvement still remains because of the lack of the reference image. Inspired by the fact that in many applications, such as parameter selection, a series of distorted images are available, the authors propose a novel comparison-based image quality assessment (C-IQA) method. The new comparison-based framework parallels FR-IQA by requiring two input images, and resembles NR-IQA by not using the original image. As a result, the new comparison-based approach has more application scenarios than FR-IQA does, and takes greater advantage of the accessible information than the traditional single-input NR-IQA does. Further, C-IQA is compared with other state-of-the-art NR-IQA methods on two widely used IQA databases. Experimental results show that C-IQA outperforms the other NR-IQA methods for parameter selection, and the parameter trimming framework combined with C-IQA saves the computation of iterative image reconstruction up to 80%.Index Terms-Image distortion, image quality assessment (IQA), human visual system (HVS), comparison-based image quality assessment (C-IQA), parameter selection

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Section: Parameter Selectionmentioning

confidence: 99%

Section: Parameter Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparison-Based Image Quality Assessment for Selecting Image Restoration Parameters

Liang¹,

Weller²

2016

IEEE Trans. on Image Process.

Self Cite

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“…At present, remarkable results have been achieved. Therefore, this paper proposes a parameter selection for a denoising algorithm based on the evaluation of no-reference image quality [10] and applies the no-reference image quality evaluation method to the image denoising algorithm [11][12][13], making it possible to adaptively select the optimal parameters. We embed it in the ROF model to experiment [14] in the search for the optimal parameters, while reducing the number of iterations of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Cascaded Algorithm for Image Quality Assessment and Image Denoising Based on CNN for Image Security and Authorization

et al. 2018

Security and Communication Networks

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With the rapid development of Internet technology, images on the Internet are used in various aspects of people's lives. The security and authorization of images are strongly dependent on image quality. Some potential problems have also emerged, among which the quality assessment and denoising of images are particularly evident. This paper proposes a novel NR-IQA method based on the dual convolutional neural network structure, which combines saliency detection with the human visual system (HSV), used as a weighting function to reflect the important distortion caused by the local area. The model is trained using gray and color features in the HSV space. It is applied to the parameter selection of an image denoising algorithm. The experiment proves that our proposed method can accurately evaluate image quality in the process of denoising. It provides great help in parameter optimization iteration and improves the performance of the algorithm. Through experiments, we obtain both improved image quality and a reasonable result of subject assessment when the cascaded algorithm is applied in image security and authorization.

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Feasibility study of a point‐of‐care positron emission tomography system with interactive imaging capability

Jiang

Komarov

et al. 2019

Medical Physics

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Purpose: We investigated the feasibility of a novel positron emission tomography (PET) system that provides near real-time feedback to an operator who can interactively scan a patient to optimize image quality. The system should be compact and mobile to support point-of-care (POC) molecular imaging applications. In this study, we present the key technologies required and discuss the potential benefits of such new capability. Methods: The core of this novel PET technology includes trackable PET detectors and a fully threedimensional, fast image reconstruction engine implemented on multiple graphics processing units (GPUs) to support dynamically changing geometry by calculating the system matrix on-the-fly using a tube-of-response approach. With near real-time image reconstruction capability, a POC-PET system may comprise a maneuverable front PET detector and a second detector panel which can be stationary or moved synchronously with the front detector such that both panels face the region-of-interest (ROI) with the detector trajectory contoured around a patient's body. We built a proof-of-concept prototype using two planar detectors each consisting of a photomultiplier tube (PMT) optically coupled to an array of 48 9 48 lutetium-yttrium oxyorthosilicate (LYSO) crystals (1.0 9 1.0 9 10.0 mm 3 each). Only 38 9 38 crystals in each arrays can be clearly re-solved and used for coincidence detection. One detector was mounted to a robotic arm which can position it at arbitrary locations, and the other detector was mounted on a rotational stage. A cylindrical phantom (102 mm in diameter, 150 mm long) with nine spherical lesions (8:1 tumor-to-background activity concentration ratio) was imaged from 27 sampling angles. List-mode events were reconstructed to form images without or with time-of-flight (TOF) information. We conducted two Monte Carlo simulations using two POC-PET systems. The first one uses the same phantom and detector setup as our experiment, with the detector coincidence re-solving time (CRT) ranging from 100 to 700 ps full-width-at-half-maximum (FWHM). The second study simulates a body-size phantom (316 9 228 9 160 mm 3 ) imaged by a larger POC-PET system that has 4 9 6 modules (32 9 32 LYSO crystals/module, four in axial and six in transaxial directions) in the front panel and 3 9 8 modules (16 9 16 LYSO crystals/module, three in axial and eight in transaxial directions) in the back panel. We also evaluated an interactive scanning strategy by progressively increasing the number of data sets used for image reconstruction. The updated images were analyzed based on the number of data sets and the detector CRT. Results: The proof-of-concept prototype re-solves most of the spherical lesions despite a limited number of coincidence events and incomplete sampling. TOF information reduces artifacts in the reconstructed images. Systems with better timing resolution exhibit improved image quality and reduced artifacts. We observed a reconstruction speed of 0.96 9 10 6 events/s/iteration for 600 9 600 9 224 voxel rect...

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Regularization parameter trimming for iterative image reconstruction

Cited by 7 publications

References 21 publications

Comparison-Based Image Quality Assessment for Selecting Image Restoration Parameters

Comparison-Based Image Quality Assessment for Selecting Image Restoration Parameters

A Cascaded Algorithm for Image Quality Assessment and Image Denoising Based on CNN for Image Security and Authorization

Feasibility study of a point‐of‐care positron emission tomography system with interactive imaging capability

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