Multi-scale HSV Color Feature Embedding for High-fidelity NIR-to-RGB Spectrum Translation

TL;DR

This paper introduces MCFNet, a multi-scale network that improves NIR-to-RGB spectrum translation by decomposing the task into sub-tasks with specialized modules, enhancing texture and color fidelity.

Contribution

The paper presents a novel multi-scale network with dedicated modules for texture, color, and geometry, addressing spectral translation ambiguities more effectively than prior methods.

Findings

Significant performance improvements over existing NIR colorization methods.

Effective multi-scale approach enhances texture and color fidelity.

Modules successfully handle spectral ambiguities in NIR-to-RGB translation.

Abstract

The NIR-to-RGB spectral domain translation is a formidable task due to the inherent spectral mapping ambiguities within NIR inputs and RGB outputs. Thus, existing methods fail to reconcile the tension between maintaining texture detail fidelity and achieving diverse color variations. In this paper, we propose a Multi-scale HSV Color Feature Embedding Network (MCFNet) that decomposes the mapping process into three sub-tasks, including NIR texture maintenance, coarse geometry reconstruction, and RGB color prediction. Thus, we propose three key modules for each corresponding sub-task: the Texture Preserving Block (TPB), the HSV Color Feature Embedding Module (HSV-CFEM), and the Geometry Reconstruction Module (GRM). These modules contribute to our MCFNet methodically tackling spectral translation through a series of escalating resolutions, progressively enriching images with color and texture fidelity in a scale-coherent fashion. The proposed MCFNet demonstrates substantial performance gains over the NIR image colorization task. Code is released at: https://github.com/AlexYangxx/MCFNet.