  In numerous substitution models for the $\l_{0}$-norm minimization problem
$(P_{0})$, the $\l_{p}$-norm minimization $(P_{p})$ with $0<p<1$ have been
considered as the most natural choice. However, the non-convex optimization
problem $(P_{p})$ are much more computational challenges, and are also NP-hard.
Meanwhile, the algorithms corresponding to the proximal mapping of the
regularization $\l_{p}$-norm minimization $(P_{p}^{\lambda})$ are limited to
few specific values of parameter $p$. In this paper, we replace the
$\ell_{p}$-norm $\|x\|_{p}^{p}$ with a modified function
$\sum_{i=1}^{n}\frac{|x_{i}|}{(|x_{i}|+\epsilon_{i})^{1-p}}$. With change the
parameter $\epsilon>0$, this modified function would like to interpolate the
$\l_{p}$-norm $\|x\|_{p}^{p}$. By this transformation, we translated the
$\l_{p}$-norm regularization minimization $(P_{p}^{\lambda})$ into a modified
$\l_{p}$-norm regularization minimization $(P_{p}^{\lambda,\epsilon})$. Then,
we develop the thresholding representation theory of the problem
$(P_{p}^{\lambda,\epsilon})$, and based on it, the IT algorithm is proposed to
solve the problem $(P_{p}^{\lambda,\epsilon})$ for all $0<p<1$. Indeed, we
could get some much better results by choosing proper $p$, which is one of the
advantages for our algorithm compared with other methods. Numerical results
also show that, for some proper $p$, our algorithm performs the best in some
sparse signal recovery problems compared with some state-of-art methods.
