Extremely Low-light Image Enhancement with Scene Text Restoration

TL;DR

This paper introduces a novel deep learning framework that significantly improves low-light image quality and scene text restoration, outperforming existing methods in image enhancement and text detection tasks.

Contribution

The proposed method uniquely combines a self-regularised attention map, edge map, and a new text detection loss to enhance both image quality and text recovery in low-light conditions.

Findings

Outperforms state-of-the-art in image restoration

Achieves superior text detection and spotting results

Effective use of synthetic images improves real low-light image enhancement

Abstract

Deep learning-based methods have made impressive progress in enhancing extremely low-light images - the image quality of the reconstructed images has generally improved. However, we found out that most of these methods could not sufficiently recover the image details, for instance, the texts in the scene. In this paper, a novel image enhancement framework is proposed to precisely restore the scene texts, as well as the overall quality of the image simultaneously under extremely low-light images conditions. Mainly, we employed a self-regularised attention map, an edge map, and a novel text detection loss. In addition, leveraging synthetic low-light images is beneficial for image enhancement on the genuine ones in terms of text detection. The quantitative and qualitative experimental results have shown that the proposed model outperforms state-of-the-art methods in image restoration, text detection, and text spotting on See In the Dark and ICDAR15 datasets.