  This paper presents a simple primal dual method named DPD which is a flexible
framework for a class of saddle point problem with or without strongly convex
component. The presented method has linearized version named LDPD and exact
version EDPD. Each iteration of DPD updates sequentially the dual and primal
variable via simple proximal mapping and refines the dual variable via
extrapolation. Convergence analysis with smooth or strongly convex primal
component recovers previous state-of-the-art results, and that with strongly
convex dual component attains full acceleration $O(1/k^2)$ in terms of primal
dual gap. Total variation image deblurring on Gaussian noisy or Salt-Pepper
noisy image demonstrate the effectiveness of the full acceleration by imposing
the strongly convexity on dual component.
