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

Accelerated Primal Dual Method for a Class of Saddle Point Problem with Strongly Convex Component