Improving Super-resolution Techniques via Employing Blurriness Information of the Image

Super-resolution (SR) is a technique that produces a high resolution (HR) image via employing a number of low resolution (LR) images from the same scene. One of the degradations that attenuates performance of the SR is the blurriness of the input LR images. In many previous works in the SR, the blurriness of the LR images is assumed to be due to the integral effect of the image sensor of the image acquisition device, while in practice, there are some other factors that blur the LR images, such as diffraction, motion of the object and/or acquisition device, atmospheric blurring and defocus blur. To apply the super-resolution process accurately, we need to know the degradation model applied on HR image leading to LR ones. In this paper, we aim to use the LR images blurriness to find the blurring kernel applied on the HR image. Hence we setup a simulation experiment in which the blurring kernel is limited to be one of the predetermined kernels. In the experiment, the blurriness of the LR images is supposed to be unknown, and is estimated using a blur kernel estimation method. Then the estimated blur kernels of the LR images are fed to an artificial neural network (ANN) to determine the blur kernels associated with the HR image. Experiment results show the use of determined blur kernels improves the quality of output HR image.