Resimlerdeki hareket bulanıklığına neden olan filtre matrislerinin yapay sinir ağları kullanılarak sınıflandırılması

When capturing a moving object with the camera, motion blur occurs in the picture depending on the speed and direction of movement. The motion blur can be classified in terms of filter matrices and the picture can be restored using it. The purpose of this project is finding the filter matrices that causes to the motion blur using artificial neural networks. Once the filter matrix or point spread function (PSF) found, the motion-blurred images can be sharpened using well-known algorithms such as Wiener, Lucy Richardson Deconvolution. A few datasets that have lots of training images are blurred with the parameters (blur angle and length) of specific motion blur kernels and they are trained using VGG, ResNet and a network model that is shared in an article. Then, the prediction of the filter matrices is evaluated using validation datasets for every trained networks. The prediction accuracies for the trained networks are compared to show which network type is best for the purpose of this thesis. Some types of artificial neural networks and their structures are investigated for their suitability in terms of finding the accurate PSF that causes motion blur. In addition, some modifications are applied to the network to increase the prediction accuracy. This improved the estimation of the correct filter matrices.