Low-Light Homomorphic Filtering Network for integrating image enhancement and classification

Abstract

Low-light image (LLI) enhancement techniques have recently demonstrated remarkable progress especially with the use of deep learning approaches. However, most existing techniques are developed as standalone solutions and do not take into account the impact of LLI enhancement on high-level computer vision tasks like object classification. In this paper, we propose a new LLI enhancement model titled LLHFNet (Low-light Homomorphic Filtering Network) which performs image-to-frequency filter learning and is designed for seamless integration into classification models. Through this integration, the classification model is embedded with an internal enhancement capability and is jointly trained to optimize both image enhancement and classification performance. We have conducted a large battery of experiments using SICE, Pascal VOC, and ExDark datasets, to quantitatively and qualitatively evaluate our approach’s enhancement quality and classification performance. When evaluated as a standalone enhancement model, our solution consistently ranks among the best existing image enhancement techniques. When embedded with a classification model, our solution achieves an average 5.5% improvement in classification accuracy, compared with the traditional pipeline of separate enhancement followed by classification. Results produce robust classification quality on both LLIs and normal-light images (NLIs), and highlight a clear improvement to the literature.