Abstract: To solve the object motion deblurring problem in static background, an object-motion deblurring approach based on transparency is proposed. Firstly, the transparency of object 1-D motion is studied and analyzed, the relationship equation between object transparency and blurred filtering is gained on the basis of conventional convolution model, and the 1-D blur filter is accurately computed with that equation. And then, the object 2-D motion is analyzed, and the upper bound for the size of the 2-D motion blur filter is estimated with transparency in the 2-D space. Finally, the unblurred transparency map and blurred filtering are estimated using conjugate gradient optimization and belief propagation, and the task of object motion deblurring is accomplished with a maximum a posterior approach of Bayesian rule. The experimental results based on both synthesized images and real images show that the proposed approach can achieve the object motion deblurring problem very well in the static background, and the performance is better than current state of the art approaches for motion deblurring.