SIAM Journal on Imaging Sciences

In order to retain as many valuable details from the input source images as possible during the process of fusion, this paper proposes an adaptive weight based total variation model for image fusion. The main idea is to employ a nonconvex energy functional ...

Although characterized by two different mathematical definitions, both the Radon and the Hough transforms ultimately take an image as input and provide, as output, functions defined on a preassigned parameter space, i.e., the so-called Radon and Hough ...

The $\ell_1-\ell_2$ regularization is a popular nonconvex yet Lipschitz continuous metric, which has been widely used in signal and image processing. The theory for the $\ell_1-\ell_2$ minimization method shows that it has superior sparse recovery ...

Performing a large number of spatial measurements enables high-resolution photoacoustic imaging without specific prior information. However, the acquisition of spatial measurements is time-consuming, costly, and technically challenging. By exploiting ...

Several imaging algorithms including patch-based image denoising, image time series recovery, and convolutional neural networks can be thought of as methods that exploit the manifold structure of signals. While the empirical performance of these ...

In coherent diffraction imaging (CDI) experiments, the intensity of the scattered wave impinging on an object is measured on an array of detectors. These measurements can be interpreted as samples of the square of the modulus of the Fourier transform of ...

We target the problem of estimating the center of mass of objects in noisy two-dimensional images. We assume that the noise dominates the image, and thus many standard approaches are vulnerable to estimation errors, e.g., the direct computation of the ...

Models related to the Euler's elastica energy have proven to be useful for many applications including image processing. Extending elastica models to color images and multichannel data is a challenging task, as stable and consistent numerical solvers for ...

In this paper, we consider minimizing the $L_1/L_2$ term on the gradient for a limited-angle scanning problem in computed tomography (CT) reconstruction. We design a specific splitting framework for an unconstrained optimization model so that the ...

In this work, we study a general framework of discrete approximations of the total variation for image reconstruction problems. The framework, for which we can show consistency in the sense of $\Gamma$-convergence, unifies and extends several existing ...

We propose an extension of a special form of gradient descent---in the literature known as linearized Bregman iteration---to a larger class of nonconvex functions. We replace the classical (squared) two norm metric in the gradient descent setting with a ...

The multiscale hierarchical decomposition method (MHDM) proposed in [E. Tadmor, S. Nezzar, and L. Vese, Multiscale Model. Simul., 2 (2004), pp. 554--579] has been proven very appropriate for denoising images with features at different scales and for scale ...