  High-resolution (HR) image harmonization is of great significance in
real-world applications such as image synthesis and image editing. However, due
to the high memory costs, existing dense pixel-to-pixel harmonization methods
are mainly focusing on processing low-resolution (LR) images. Some recent works
resort to combining with color-to-color transformations but are either limited
to certain resolutions or heavily depend on hand-crafted image filters. In this
work, we explore leveraging the implicit neural representation (INR) and
propose a novel image Harmonization method based on Implicit neural Networks
(HINet), which to the best of our knowledge, is the first dense pixel-to-pixel
method applicable to HR images without any hand-crafted filter design. Inspired
by the Retinex theory, we decouple the MLPs into two parts to respectively
capture the content and environment of composite images. A Low-Resolution Image
Prior (LRIP) network is designed to alleviate the Boundary Inconsistency
problem, and we also propose new designs for the training and inference
process. Extensive experiments have demonstrated the effectiveness of our
method compared with state-of-the-art methods. Furthermore, some interesting
and practical applications of the proposed method are explored. Our code is
available at https://github.com/WindVChen/INR-Harmonization.
