A regularized deep learning approach for image de-blurring
Francesco Lo Conti, Gabriele Minucci, Naser Derakhshan · 2017
In this paper, a novel convolutional neural network model for blind deconvolution of images is proposed. The structure of the model is based on two sub models devoted, respectively, to deblurring and denoising of an input image. The model has been designed to restore a picture affected by different kinds of noise. The main innovation is the introduction of a regularization term in the training cost function, based on a blurred/non-blurred classification tool. Results show interesting features of the model, particularly regarding the robustness of results. The comparison with other state-of-the-art models confirms the value of the model proposed in this study.