Morphing from the TV-Norm to the l 0 -Norm.

Abstract

For piecewise constant objects, the images can be reconstructed with under-sampled measurements. The gradient image of a piecewise image is sparse. If a sparse solution is a desired solution, an -norm minimization method is effective to solve an under-determined system. However, the -norm is not differentiable, and it is not straightforward to minimize an -norm. This paper suggests a function that is like the -norm function, and we refer to this function as meta -norm. The subdifferential of the meta -norm has a simple explicit expression. Thus, it is straightforward to derive a gradient descent algorithm to enforce the sparseness in the solution. In fact, the proposed meta norm is a transition that varies between the TV-norm and the -norm. As an application, this paper uses the proposed meta -norm for few-view tomography. Computer simulation results indicate that the proposed meta -norm effectively guides the image reconstruction algorithm to a piecewise constant solution. It is not clear whether the TV-norm or the -norm is more effective in producing a sparse solution. Index Terms-Inverse problem, optimization, total-variation minimization, -norm minimization, few-view tomography, iterative algorithm, image reconstruction.