Cross-Camera Deep Colorization

Abstract

. In this paper, we consider the color-plus-mono dual-camera system and propose an end-to-end convolutional neural network to align and fuse images from it in an efficient and cost-effective way. Our method takes cross-domain and cross-scale images as input, and consequently synthesizes HR colorization results to facilitate the trade-off between spatial-temporal resolution and color depth in the single-camera imaging system. In contrast to the previous colorization methods, ours can adapt to color and monochrome cameras with distinctive spatial-temporal resolutions, rendering the flexibility and robustness in practical applications. The key ingredient of our method is a cross-camera alignment module that generates multi-scale correspondences for cross-domain image alignment. Through extensive experiments on various datasets and multiple settings, we val-idate the flexibility and effectiveness of our approach. Remarkably, our method consistently achieves substantial improvements, i.e. , around 10dB PSNR gain, upon the state-of-the-art methods. Code is at: github.com/IndigoPurple/CCDC.