Mitigating the Drawbacks of the L Norm and the Total Variation Norm.

In compressed sensing, it is believed that the norm minimization is the best way to enforce a sparse solution. However, the norm is difficult to implement in a gradient-based iterative image reconstruction algorithm. The total variation (TV) norm minimization is considered a proper substitute for the norm minimization. This paper points out that the TV norm is not powerful enough to enforce a piecewise-constant image. This paper uses the limited-angle tomography to illustrate the possibility of using the norm to encourage a piecewise-constant image. However, one of the drawbacks of the norm is that its derivative is zero almost everywhere, making a gradient-based algorithm useless. Our novel idea is to replace the zero value of the norm derivative with a zero-mean random variable. Computer simulations show that the proposed norm minimization outperforms the TV minimization. The novelty of this paper is the introduction of some randomness in the gradient of the objective function when the gradient is zero. The quantitative evaluations indicate the improvements of the proposed method in terms of the structural similarity (SSIM) and the peak signal-to-noise ratio (PSNR).