Random Projector: Efficient Deep Image Prior

Deep image prior (DIP) has shown great promise in tackling a range of image restoration problems. However, its optimization is extremely sluggish, which inevitability hinders its practical usage when there are hard time constraints. To mitigate this issue, we propose a more compact and efficient model, dubbed random projector (RP), and freeze the convolutional layers of the neural network to prevent slow learning. We further make use of an explicit prior---total variation---to regularize the reconstructed natural images and promote pleasure-looking images. We evaluate our proposed method on different image restoration tasks such as image denoising and image inpainting, and conduct comparisons with DIP and its prevalent variants. The experimental results suggest that our proposed random projector achieves competitive restoration quality in terms of PSNR while it significantly reduces the optimization (OPT) time.