JPEG Information Regularized Deep Image Prior for Denoising
Tsukasa Takagi Shinya Ishizaki Shin-ichi Maeda
Image denoising is a representative image restoration task in computer
vision. Recent progress of image denoising from only noisy images has attracted
much attention. Deep image prior (DIP) demonstrated successful image denoising
from only a noisy image by inductive bias of convolutional neural network
architectures without any pre-training. The major challenge of DIP based image
denoising is that DIP would completely recover the original noisy image unless
applying early stopping. For early stopping without a ground-truth clean image,
we propose to monitor JPEG file size of the recovered image during optimization
as a proxy metric of noise levels in the recovered image. Our experiments show
that the compressed image file size works as an effective metric for early
stopping.