  Coordinate-based Multilayer Perceptron (MLP) networks, despite being capable
of learning neural implicit representations, are not performant for internal
image synthesis applications. Convolutional Neural Networks (CNNs) are
typically used instead for a variety of internal generative tasks, at the cost
of a larger model. We propose Neural Knitwork, an architecture for neural
implicit representation learning of natural images that achieves image
synthesis by optimizing the distribution of image patches in an adversarial
manner and by enforcing consistency between the patch predictions. To the best
of our knowledge, this is the first implementation of a coordinate-based MLP
tailored for synthesis tasks such as image inpainting, super-resolution, and
denoising. We demonstrate the utility of the proposed technique by training on
these three tasks. The results show that modeling natural images using patches,
rather than pixels, produces results of higher fidelity. The resulting model
requires 80% fewer parameters than alternative CNN-based solutions while
achieving comparable performance and training time.

Computer Vision and Pattern Recognition;Artificial Intelligence;Graphics