Conventionally, the 3D object reconstructed from computational integral imaging technique consists of focused and off-focused areas. The reconstructed object with off-focused points will affect the high-level image analysis such as object classification, recognition, and tracking. Therefore, it is necessary to develop a method to remove the off-focused points for further object analysis. For each point in 3D space, we assume that its intensity values on all the 2D elemental images captured with integral imaging system are similar. Consequently, each focused point on reconstructed depth slice image will share the sample points on elemental images with similar intensity values while the sample points of each off-focused point will have large varied intensity values. If the variance of these sample points on elemental images is larger than a pre-defined threshold, the corresponding point on the reconstructed depth slice image can be estimated as the off-focused point and removed. However, each point on the reconstructed image doing the similar processing sequentially will make the computation burden, especially to multiple depth slice images reconstruction. In this paper, we overview a method to reconstruct the multiple depth slice images with only focused parts in parallel using graphic processing unit (GPU). Experimental results show that this method can reconstruct the multiple depth slice images without off-focused points in a much faster speed on GPU than that on CPU.