Colour-INR - Implicit Neural Representation for Image Colorization through Colour Guidance

Colourization of black and white images can be considered as an inverse problem that searches for pixel-level colour values based on observed intensity mappings. Since the forward problem follows a many-to-one mapping, the inverse admits multiple possible chrominance solutions. However, there is a set of solutions that can be considered realistic and acceptable. When defining this set of solutions, human perception becomes an important factor. Hint-based image colourization provides guidance to constrain this set of solutions. Implicit Neural Representations (INRs) have been applied to multiple inverse tasks such as denoising, super-resolution, and inpainting. However, their use for image colourization remains underexplored. In this research, we show that INRs have the potential to perform image colourization when guided appropriately. Moving further, we introduce Colour-INR, where a coordinate-based INR predicts chrominance from spatial coordinates, sparse colour hints provide chrominance supervision, and grayscale-derived edges guide spatial regularization. On Kodak with $0.5\%$ colour hints, Colour-INR achieves a mean PSNR of $32.64$ dB and substantially outperforms the evaluated pretrained learning-based baselines, while remaining competitive with the classical optimization method of Levin et al. Further, we experimentally show that Colour-INR can be optimized directly on high-resolution DIV2K images at their native resolution without changing the network architecture or requiring dataset-level retraining. These results demonstrate the potential of properly guided INRs for image colourization.