Denoising has to do with estimating a signal $x_0$ from its noisy
observations $y=x_0+z$. In this paper, we focus on the "structured denoising
problem", where the signal $x_0$ possesses a certain structure and $z$ has
independent normally distributed entries with mean zero and variance
$\sigma^2$. We employ a structure-inducing convex function $f(\cdot)$ and solve
$\min_x\{\frac{1}{2}\|y-x\|_2^2+\sigma\lambda f(x)\}$ to estimate $x_0$, for
some $\lambda>0$. Common choices for $f(\cdot)$ include the $\ell_1$ norm for
sparse vectors, the $\ell_1-\ell_2$ norm for block-sparse signals and the
nuclear norm for low-rank matrices. The metric we use to evaluate the
performance of an estimate $x^*$ is the normalized mean-squared-error
$\text{NMSE}(\sigma)=\frac{\mathbb{E}\|x^*-x_0\|_2^2}{\sigma^2}$. We show that
NMSE is maximized as $\sigma\rightarrow 0$ and we find the \emph{exact} worst
case NMSE, which has a simple geometric interpretation: the
mean-squared-distance of a standard normal vector to the $\lambda$-scaled
subdifferential $\lambda\partial f(x_0)$. When $\lambda$ is optimally tuned to
minimize the worst-case NMSE, our results can be related to the constrained
denoising problem $\min_{f(x)\leq f(x_0)}\{\|y-x\|_2\}$. The paper also
connects these results to the generalized LASSO problem, in which, one solves
$\min_{f(x)\leq f(x_0)}\{\|y-Ax\|_2\}$ to estimate $x_0$ from noisy linear
observations $y=Ax_0+z$. We show that certain properties of the LASSO problem
are closely related to the denoising problem. In particular, we characterize
the normalized LASSO cost and show that it exhibits a "phase transition" as a
function of number of observations. Our results are significant in two ways.
First, we find a simple formula for the performance of a general convex
estimator. Secondly, we establish a connection between the denoising and linear
inverse problems.

本研究关注带有一定结构信号的去噪问题，使用结构诱导凸函数来最小化误差和正则项以评估模型性能，并建立了去噪与线性反问题之间的联系。