One method of solving the single-image super-resolution problem is to use Heaviside functions. This has been done previously by making a binary classification of image components as "smooth" and "non-smooth", describing these with approximated Heaviside functions (AHFs), and iteration including l1 regularization. We now introduce a new method in which the binary classification of image componen...

Functional neuroimaging data embodies a massive multiple testing problem, where 100,000 correlated test statistics must be assessed. The familywise error rate, the chance of any false positives is the standard measure of Type I errors in multiple testing. In this paper we review and evaluate three approaches to thresholding images of test statistics: Bonferroni, random field and the permutation...

Two central issues in stereo algorithm design are the matching criterion and the underlying smoothness assumptions. In this paper we propose a new stereo algorithm with novel approaches to both issues. We start with a careful analysis of the properties of the continuous disparity space image (DSI), and derive a new matching cost based on the reconstructed image signals. We then use a symmetric ...

Graph cut minimization formulates the segmentation problem as the liner combination of data and smoothness terms. The smoothness term is included in the energy formulation through a regularization parameter. We propose that the trade-off between the data and the smoothness terms should not be balanced by the same regularization parameter for the whole image. In order to validate the proposed id...

Nonnegative Tensor Factorization (NTF) is an emerging technique in multidimensional signal analysis and it can be used to find partsbased representations of high-dimensional data. Inmany applications such as multichannel spectrogram processing or multiarray spectra analysis, the unknown features have locally smooth temporal or spatial structure. In this paper, we incorporate to an objective fun...

In this paper we have presented a method for fusing two dimensional multi-resolution 2-D images using wavelet transform under the combine gradient and smoothness criterion. The usefulness of the method has been illustrated using various experimental image pairs such as the multifocus images, multi-sensor satellite image and CT and MR images of cross-section of human brain. The results of the pr...

Recently, a theory, developed by DeVore, Jawerth, and Popov, of nonlinear approximation by both orthogonal and nonorthogonal wavelets has been applied to problems in surface and image compression by DeVore, Jawerth, and Lucier. This theory relates precisely the norms in which the error is measured, the rate of decay in that error as the compression decreases, and the smoothness of the data. In ...

In blocking artifacts reduction based on the projection onto convex sets (POCS) technique, good constraint sets are very important. Until recently, smoothness constraint sets (SCS) are often formulated in the image domain, whereas quantization constraint set is defined in the block-based discrete cosine transform (BDCT) domain. Thus, frequent BDCT transform is inevitable in alternative projecti...

The process of using one image to guide the filtering process of another one is called Guided Image Filtering (GIF). The main challenge of GIF is the structure inconsistency between the guidance image and the target image. Besides, noise in the target image is also a challenging issue especially when it is heavy. In this paper, we propose a general framework for Robust Guided Image Filtering (R...

Resumo: In this work, we address the problem of obtaining perceptually acceptable panoramic images which are images of wide fields of view (FOVs). To achieve this goal, we first model the visible information of a scene as a unit sphere and then model the panoramic images as projections from this sphere to an image plane. Desirable properties in panoramic images are transformed in properties tha...