VOS: Towards thermal infrared image colorization via View Overlap Strategy

Abstract

Currently, a significant challenge still exists in thermal infrared images colorization, as current methods struggle with translating texture naturally and achieving color accuracy. To overcome this challenge, we propose a View Overlap Strategy (VOS) for colorizing infrared images. The proposed VOS employs a dual-branch generator designed to translate different regions of the same object into colorization output, and it evaluates the generated overlapping regions through an Optimal Adversarial Strategy (OAS) to determine the best generator output results. To achieve whole-image colorization, a unique sliding mechanism is designed that gradually extends the colorized region over the entire infrared image, continually approximating the final colorization result during the dual-branch generator’s adversarial training. Extensive experiments on the FLIR dataset and KAIST dataset demonstrate that the proposed VOS can be applied within existing colorization adversarial networks, leading to superior performance metrics and visual quality. The color images generated through our proposed VOS present enhanced clarity and realism.