In this paper, we propose a new regularization framework that regularizes mask directly by applying a mask filtering technique to improve computational efficiency and enhance mask manufacturability for pixel-based Inverse Lithography Technique (ILT). Generally, the synthesized mask by pixel-based ILT is a grey-level image, and possesses small, unwanted block objects, such as isolated holes, protrusions, and jagged edges, which are unreachable in the real manufacturing process. The proposed method filters (or regularizes) mask directly to guarantee manufacturability of the synthesized mask pattern; this technique is different from the conventional regularization method that regularizes mask by incorporating various penalty functions to a cost function. A tailored mask filter is developed in this special ILT case. In addition, we introduce a new metric, edge distance error which has the same dimension nanometer as edge placement error and has a continuous expression as pattern error, to guide mask synthesis. Simulation results demonstrating the validity and efficiency of the proposed method are presented.