INRetouch: Context Aware Implicit Neural Representation for Photography Retouching

TL;DR

This paper introduces INRetouch, a context-aware implicit neural representation that learns from professional edits to automate complex photo retouching with high fidelity, using a large dataset and adaptive transformations.

Contribution

It presents a novel implicit neural approach for photo retouching that learns from a large dataset and adapts to image content, outperforming existing methods in fidelity and control.

Findings

Outperforms existing retouching methods in fidelity and control

Successfully learns from a single example and adapts to new images

Enhances performance in related image reconstruction tasks

Abstract

Professional photo editing remains challenging, requiring extensive knowledge of imaging pipelines and significant expertise. While recent deep learning approaches, particularly style transfer methods, have attempted to automate this process, they often struggle with output fidelity, editing control, and complex retouching capabilities. We propose a novel retouch transfer approach that learns from professional edits through before-after image pairs, enabling precise replication of complex editing operations. We develop a context-aware Implicit Neural Representation that learns to apply edits adaptively based on image content and context, and is capable of learning from a single example. Our method extracts implicit transformations from reference edits and adaptively applies them to new images. To facilitate this research direction, we introduce a comprehensive Photo Retouching Dataset comprising 100,000 high-quality images edited using over 170 professional Adobe Lightroom presets. Through extensive evaluation, we demonstrate that our approach not only surpasses existing methods in photo retouching but also enhances performance in related image reconstruction tasks like Gamut Mapping and Raw Reconstruction. By bridging the gap between professional editing capabilities and automated solutions, our work presents a significant step toward making sophisticated photo editing more accessible while maintaining high-fidelity results. The source code and the dataset are publicly available at https://omaralezaby.github.io/inretouch .