Blurry Facial-Image Deconvolution via Model-Guided Deep Neural Network Inspired From Edge Regularization

The blind facial-image deconvolution problem is ill-posed so that the estimation of latent image and blur kernel is hard to obtain without additional priors or heuristics. Although data-driven methods with deep neural networks have achieved excellent performance in this issue, most existing methods are designed like black boxes without transparency and interpretability. Thus, a model-guided explainable approach is proposed to address the above issues on blurry-facial images’ recovery, named the model-guided deep neural network (MG-DNN). To break the barrier between the network design and interpretability, we optimize blind image deconvolution based on salient edge regularization (BID-SER) and use it to guide the proposed MG-DNN. First, basic units for processing image and kernel features are designed with convolutional operators. Then, unfolding the optimization of BID-SER, the multiblocks concatenation is designed by several kernel networks and image networks with basic units. Finally, comprehensive experiments evaluated by several metrics demonstrate that the proposed MG-DNN performs favorably with existing state-of-the-art methods while processing on three different face data sets.