Super-resolution is a process that combines information from some low-resolution images in order to produce an image with higher resolution. In most of the previous related work, the blurriness that is associated with low resolution images is assumed to be due to the integral effect of the acquisition device’ s image sensor. However, in practice there are other sources of blurriness as well, including atmospheric and motion blur that may be applied to low resolution images. The research done in this paper provides a Super-resolution image from some low-resolution images suffering from blurriness due to defocus. In contrast to motion blur kernels that are sparse, the defocus blur kernel is non-sparse and continuous. Because of the continuity property of defocus blurring kernel, in this paper, we bound the gradient of blurring kernel using proper regularizers to satisfy this property. Experimental results on synthetic data demonstrate the effectiveness of the proposed method to produce high resolution and de-blurred images from some blurry low-resolution images.