  We propose SinIR, an efficient reconstruction-based framework trained on a
single natural image for general image manipulation, including
super-resolution, editing, harmonization, paint-to-image, photo-realistic style
transfer, and artistic style transfer. We train our model on a single image
with cascaded multi-scale learning, where each network at each scale is
responsible for image reconstruction. This reconstruction objective greatly
reduces the complexity and running time of training, compared to the GAN
objective. However, the reconstruction objective also exacerbates the output
quality. Therefore, to solve this problem, we further utilize simple random
pixel shuffling, which also gives control over manipulation, inspired by the
Denoising Autoencoder. With quantitative evaluation, we show that SinIR has
competitive performance on various image manipulation tasks. Moreover, with a
much simpler training objective (i.e., reconstruction), SinIR is trained 33.5
times faster than SinGAN (for 500 X 500 images) that solves similar tasks. Our
code is publicly available at github.com/YooJiHyeong/SinIR.
