CompuCorGP

This paper provides an overview on super-resolution (SR) research in medical imaging applications. Many imaging modalities exist. Some provide anatomical information and reveal information about the structure of the human body, and others provide functional information, locations of activity for specific activities and specified tasks. Each imaging system has a characteristic resolution, which is determined based on physical constraints of the system detectors that are in turn tuned to signal-to-noise and timing considerations. A common goal across systems is to increase the resolution, and as much as possible achieve true isotropic 3-D imaging. SR technology can serve to advance this goal. Research on SR in key medical imaging modalities, including MRI, fMRI and PET, has started to emerge in recent years and is reviewed herein. The algorithms used are mostly based on standard SR algorithms. Results demonstrate the potential in introducing SR techniques into practical medical applications.

Representing the image to be inpainted in an appropriate sparse representation dictionary, and combining elements from Bayesian statistics and modern harmonic analysis, we introduce an expectation maximization (EM) algorithm for image inpainting and interpolation. From a statistical point of view, the inpainting/interpolation can be viewed as an estimation problem with missing data. Toward this goal, we propose the idea of using the EM mechanism in a Bayesian framework, where a sparsity promoting prior penalty is imposed on the reconstructed coefficients. The EM framework gives a principled way to establish formally the idea that missing samples can be recovered/interpolated based on sparse representations. We first introduce an easy and efficient sparse-representation-based iterative algorithm for image inpainting. Additionally, we derive its theoretical convergence properties. Compared to its competitors, this algorithm allows a high degree of flexibility to recover different structural components in the image (piecewise smooth, curvilinear, texture, etc.). We also suggest some guidelines to automatically tune the regularization parameter.

In this paper, we study the Papoulis–Gerchberg (PG) method and its applications to domains of image restoration such as super-resolution (SR) and inpainting. We show that the method performs well under certain conditions. We then suggest improvements to the method to achieve better SR and inpainting results. The modification applied to the SR process also allows us to apply the method to a larger class of images by doing away with some of the restrictions inherent in the classical PG method. We also present results to demonstrate the performance of the proposed techniques.

In this paper, we propose a super-resolution image reconstruction algorithm to moderate-resolution imaging spectroradiometer (MODIS) remote sensing images. This algorithm consists of two parts: registration and reconstruction. In the registration part, a truncated quadratic cost function is used to exclude the outlier pixels, which strongly deviate from the registration model. Accurate photometric and geometric registration parameters can be obtained simultaneously. In the reconstruction part, the L₁ norm data fidelity term is chosen to reduce the effects of inevitable registration error, and a Huber prior is used as regularization to preserve sharp edges in the reconstructed image. In this process, the outliers are excluded again to enhance the robustness of the algorithm. The proposed algorithm has been tested using real MODIS band-4 images, which were captured in different dates. The experimental results and comparative analyses verify the effectiveness of this algorithm.

We present a novel method of Bayesian image super-resolution in which marginalization is carried out over latent parameters such as geometric and photometric registration and the image point-spread function. Related Bayesian super-resolution approaches marginalize over the high-resolution image, necessitating the use of an unfavourable image prior, whereas our method allows for more realistic image prior distributions, and reduces the dimension of the integral considerably, removing the main computational bottleneck of algorithms such as Tipping and Bishop’s Bayesian image super-resolution. We show results on real and synthetic datasets to illustrate the efficacy of our method.

For the last two decades, two related approaches have been studied independently in conjunction with limitations of image sensors. The one is to reconstruct a high-resolution (HR) image from multiple low-resolution (LR) observations suffering from various degradations such as blur, geometric deformation, aliasing, noise, spatial sampling and so on. The other one is to reconstruct a high dynamic range (HDR) image from differently exposed multiple low dynamic range (LDR) images. LDR is due to the limitation of the capacitance of analogue-to-digital converter and the nonlinearity of the imaging system’s response function. In practical situations, since observations suffer from limitations of both spatial resolution and dynamic range, it is reasonable to address them in a unified context. Most super-resolution (SR) image reconstruction methods that enhance the spatial resolution assume that the dynamic ranges of observations are the same or the imaging system’s response function is already known. In this paper, the conventional approaches are overviewed and the SR image reconstruction, which simultaneously enhances spatial resolution and dynamic range, is proposed. The image degradation process including limited spatial resolution and limited dynamic range is modelled. With the observation model, the maximum a posteriori estimates of the response function of the imaging system as well as the single HR image and HDR image are obtained. Experimental results indicate that the proposed algorithm outperforms the conventional approaches that perform the HR and HDR reconstructions sequentially with respect to both objective and subjective criteria.

Super-resolution (SR) is the area of research and development which produces one or a set of high-resolution images from one or a set of low-resolution frames. In this paper, first, a short review of a variety of SR problems is presented. Then, starting by a single input single output case, we present different forward modeling of 1D or 2D SR problems. We focus then on the multi input single output and multi input multi output SR problems and provide a summary of recent contributions to them. Then, the SR problem is considered as an inverse problem. A general forward-modeling and inversion framework is presented, which gives the possibility to understand the basics of several classical SR methods and to discuss some important open problems of SR. Specifically, we discuss a different forward modeling, which leads to different classical methods and present our recent inversion methods based on the Bayesian estimation with different prior modeling. In particular, we give the details of a new method, particularly appropriate for piecewise homogeneous images, which provides not only an SR image, but also simultaneously an optimal segmentation of an HR image. Some comparisons of the relative performances of these methods are also presented. Finally, some future challenges in SR are outlined and discussed.

Since you can add applications, documents, or folders to the Sidebar, drag them into the Dock, or create aliases for them on your Desktop, it’s easy to forget where you actually store items you may use every day. Luckily, Mac OS X Leopard offers three different ways you can trace the path of a file, application, or folder. And you can learn all three by watching the latest Quick Tip of the Week.

I was very happy to see that President Obama kept his Blackberry, and is using it with super good security controls.

I am even happier to see that he wants to make sure the U.S. has strong cybersecurity in place; I sure hope this helps business leaders to see the need for strong security within their own organizations…

Just something to know…