Semantic-Sparse Colorization Network for Deep Exemplar-based Colorization

TL;DR

This paper introduces the Semantic-Sparse Colorization Network (SSCN), which improves exemplar-based colorization by accurately matching semantic features and balancing global and local colors, achieving state-of-the-art results.

Contribution

The paper presents a novel SSCN that addresses inaccurate luminance-based correspondence and reduces wrong matches, enhancing colorization quality.

Findings

Outperforms existing methods quantitatively

Achieves superior qualitative colorization results

Balances global style and local details effectively

Abstract

Exemplar-based colorization approaches rely on reference image to provide plausible colors for target gray-scale image. The key and difficulty of exemplar-based colorization is to establish an accurate correspondence between these two images. Previous approaches have attempted to construct such a correspondence but are faced with two obstacles. First, using luminance channels for the calculation of correspondence is inaccurate. Second, the dense correspondence they built introduces wrong matching results and increases the computation burden. To address these two problems, we propose Semantic-Sparse Colorization Network (SSCN) to transfer both the global image style and detailed semantic-related colors to the gray-scale image in a coarse-to-fine manner. Our network can perfectly balance the global and local colors while alleviating the ambiguous matching problem. Experiments show that our method outperforms existing methods in both quantitative and qualitative evaluation and achieves state-of-the-art performance.