A blur restoration scheme for images with linear motion blurred objects in still background is proposed. The proposed scheme starts from a rough detection of locations of blurred objects. This rough segmentation of the blurred regions is based on an analysis of local orientation map. Then, parameters of blur are identified based on a linear constant-velocity motion blur model for every detected blurred region. After the blur parameters are estimated, the locations of blurred objects can be refined before going to a restoration process by using information from the identified blur parameters. Blur locations are refined by observing local power of the blurred image which is filtered by a high-pass filter. The high-pass filter has approximately a frequency characteristic that is complementary to the identified blur point spread function. As a final step, the image is restored by using the estimated blur parameters and locations based on an iterative deconvolution scheme applied with a regularization concept. Experimental examples of simulated and real world blurred images are demonstrated to confirm the performance of the proposed scheme.