Combining Deep Image Prior and Second-Order Generalized Total Variance for Image Denoising

Deep image prior is a classical unsupervised deep learning method that does not require plenty of training samples, because in some practical applications, like medical imaging, collecting tons of training samples is not always viable. At the same time, it is hard and time-consuming to construct a favorable training set, since it requires that the selected data must be sufficient and typical. In addition, due to the overfitting problem of the deep image prior method, it can lead to the loss of image texture details and the destruction of edge information. And the second-order total generalized variation can effectively protect the image edge information. Therefore, in this work, an image denoising method that combines a deep image prior with the second-order total generalized variation is proposed to remove noise, preserve the image edge and texture structuration a better way. To solve the new model efficiently, first, we transform the constrained problem into an unconstrained problem using the augmented Lagrangian method, and then solve the augmented Lagrangian function using the alternating direction method of multiplication. Experimental results show that the proposed method can removes noise more effectively, retain more image details, and obtain higher performance in terms of peak signal-to-noise ratio and structural similarity, and outperform other existing methods.