255 research outputs found
Recent Progress in Image Deblurring
This paper comprehensively reviews the recent development of image
deblurring, including non-blind/blind, spatially invariant/variant deblurring
techniques. Indeed, these techniques share the same objective of inferring a
latent sharp image from one or several corresponding blurry images, while the
blind deblurring techniques are also required to derive an accurate blur
kernel. Considering the critical role of image restoration in modern imaging
systems to provide high-quality images under complex environments such as
motion, undesirable lighting conditions, and imperfect system components, image
deblurring has attracted growing attention in recent years. From the viewpoint
of how to handle the ill-posedness which is a crucial issue in deblurring
tasks, existing methods can be grouped into five categories: Bayesian inference
framework, variational methods, sparse representation-based methods,
homography-based modeling, and region-based methods. In spite of achieving a
certain level of development, image deblurring, especially the blind case, is
limited in its success by complex application conditions which make the blur
kernel hard to obtain and be spatially variant. We provide a holistic
understanding and deep insight into image deblurring in this review. An
analysis of the empirical evidence for representative methods, practical
issues, as well as a discussion of promising future directions are also
presented.Comment: 53 pages, 17 figure
Multi-frame-based Cross-domain Image Denoising for Low-dose Computed Tomography
Computed tomography (CT) has been used worldwide for decades as one of the
most important non-invasive tests in assisting diagnosis. However, the ionizing
nature of X-ray exposure raises concerns about potential health risks such as
cancer. The desire for lower radiation dose has driven researchers to improve
the reconstruction quality, especially by removing noise and artifacts.
Although previous studies on low-dose computed tomography (LDCT) denoising have
demonstrated the effectiveness of learning-based methods, most of them were
developed on the simulated data collected using Radon transform. However, the
real-world scenario significantly differs from the simulation domain, and the
joint optimization of denoising with modern CT image reconstruction pipeline is
still missing. In this paper, for the commercially available third-generation
multi-slice spiral CT scanners, we propose a two-stage method that better
exploits the complete reconstruction pipeline for LDCT denoising across
different domains. Our method makes good use of the high redundancy of both the
multi-slice projections and the volumetric reconstructions while avoiding the
collapse of information in conventional cascaded frameworks. The dedicated
design also provides a clearer interpretation of the workflow. Through
extensive evaluations, we demonstrate its superior performance against
state-of-the-art methods
Patch-based image reconstruction for PET using prior-image derived dictionaries
This collection contains figures and reconstructed images in .mat format associated with the manuscript tiled "Patch-based image reconstruction for PET using prior-image derived dictionaries" . The file, Data_Fig9-10.zip contains the reconstructed images associated with Fig 9 and 10 as a function of iteration for different methods. Data_Fig10-12.zip contains reconstructed images of the real data for different methods
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