Sub-Dip: Optimization On A Subspace With Deep Image Prior Regularization And Application To Superresolution

The Deep Image Prior has been recently introduced to solve inverse problems in image processing with no need for training data other than the image itself. However, the original training algorithm of the Deep Image Prior constrains the reconstructed image to be on a manifold described by a convolutional neural network. For some problems, this neglects prior knowledge and can render certain regularizers ineffective. This work proposes an alternative approach that relaxes this constraint and fully exploits all prior knowledge. We evaluate our algorithm on the problem of reconstructing a high-resolution image from a downsampled version and observe a significant improvement over the original Deep Image Prior algorithm.