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May 25, 2011 · This dissertation concerns the use of constrained imaging models to guide the design of both data acquisition and image reconstruction, leading ...
Justin P. Haldar: Constrained imaging: denoising and sparse sampling. University of Illinois Urbana-Champaign, USA, 2011.
We propose in this paper a novel model for image denoising, designated as the bilateral weighted sparse coding and low-rank (BWSCLR) model.
In summary, our proposed unsupervised deep learning-based denoising and reconstruction framework can improve the image quality and accelerate the imaging speed ...
This chapter provides a tutorial overview of the early constrained image reconstruction methods used in MRI.
Jul 8, 2019 · Many current image denoising methods exploit the sparsity prior of natural images. Sparse representation-based methods encode an image over an ...
Jun 30, 2017 · In recent years, the sparse coding-based techniques have been widely used for image denoising. However, most of the sparse coding-based ...
The BM3D-prGAMP algorithm incorporates the BM3D image denoising method into the iteration of the GAMP (generalized approximate message passing) algorithm. The ...
Using dictionary learning, one can denoise DW images effectively and perform faster acquisitions. Higher b-value acquisitions and DSI techniques are possible ...
Missing: sampling | Show results with:sampling
Sparse-view computed tomography (CT) is an important way to reduce the negative effect of radiation exposure in medical imaging by skipping some X-ray ...