Detection of motion blur direction based on maxima locations for blind deconvolution

The blurs in images closely resemble an ideal point spread function (PSF) model. This similarity can be exploited in the deconvolution process by learning a model that best fits the estimated PSF. In order to achieve this, a model is selected from the provided training set and then integrated into the reconstruction cost function. In this paper, we propose to eliminate the need for a training set and instead use a reference PSF (RPSF) in its place. This eliminates the need for specifying a training set as well as the dependence on estimated quantities. Furthermore, it is only dependent on the given degraded image assuming that it is uniformly blurred. The method is tested with motion blurs in different directions since it is one of the most commonly encountered problems when using consumer cameras. Using the blur support as a priori knowledge, the results show that the proposed method is capable of accurately determining the motion direction even in the presence of noise. The reconstruction of the image is achieved by using a modified cost function that also accounts for the contour of the estimated PSF. Results show that higher image quality and lower PSF estimation error can be obtained