Motion artifacts are a significant issue in medical image reconstruction. There are many methods for incorporating motion information into image reconstruction. However, there are fewer studies that focus on deformation regularization in motioncompensated image reconstruction. The usual choice for deformation regularization has been penalty functions based on the assumption that tissues are ela...

Morphological reconstruction is part of a set of image operators often referred to as geodesic. In the binary case, reconstruction simply extracts the connected components of a binary image I (the mask) which are \marked" by a (binary) image J contained in I. This transformation can be extended to the grayscale case, where it turns out to be extremely useful for several image analysis tasks. Th...

Image super-resolution (SR) reconstruction is to reconstruct a high-resolution (HR) image from one or a series of low-resolution (LR) images in the same scene with a certain amount of prior knowledge. Learning based algorithm is an effective one in image super-resolution reconstruction algorithm. The core idea of the algorithm is to use the training examples of image to increase the high freque...

For hyperspectral image research, spectral characteristic retainment is more important than the spatial details retainment, so it is necessary to evaluate the spectral influence of hyperspectral image compressed sensing. In this paper, the researchers select a hyperspectral remote sensing image PROBE CHRIS with abundant coastal wetland ground objects to analyze spectral fidelity of wavelet tran...

introduction: an efficient method of tomographic imaging in nuclear medicine is positron emission tomography (pet). compared to spect, pet has the advantages of higher levels of sensitivity, spatial resolution and more accurate quantification. however, high noise levels in the image limit its diagnostic utility. noise removal in nuclear medicine is traditionally based on fourier decomposition o...

Regularization methods are commonly used in X-ray CT image reconstruction. Different regularization methods reflect the characterization of different prior knowledge of images. In a recent work, a new regularization method called a low-dimensional manifold model (LDMM) is investigated to characterize the low-dimensional patch manifold structure of natural images, where the manifold dimensionali...

An efficient energy-saving method is proposed by taking the advantages of lightweight encoder and high compression efficiency of Compressed Sensing, which is extremely suitable for image nodes in Wireless Multi-media Sensor Networks. However, the Orthogonal Matching Pursuit (OMP) reconstruction algorithms in CS can not balance the reconstruction image quality and processing time. A novel recons...