  There are quite a number of photographs captured under undesirable conditions
in the last century. Thus, they are often noisy, regionally incomplete, and
grayscale formatted. Conventional approaches mainly focus on one point so that
those restoration results are not perceptually sharp or clean enough. To solve
these problems, we propose a noise prior learner NEGAN to simulate the noise
distribution of real legacy photos using unpaired images. It mainly focuses on
matching high-frequency parts of noisy images through discrete wavelet
transform (DWT) since they include most of noise statistics. We also create a
large legacy photo dataset for learning noise prior. Using learned noise prior,
we can easily build valid training pairs by degrading clean images. Then, we
propose an IEGAN framework performing image editing including joint denoising,
inpainting and colorization based on the estimated noise prior. We evaluate the
proposed system and compare it with state-of-the-art image enhancement methods.
The experimental results demonstrate that it achieves the best perceptual
quality.
https://github.com/zhaoyuzhi/Legacy-Photo-Editing-with-Learned-Noise-Prior for
the codes and the proposed LP dataset.
