Learning to Fuse Heterogeneous Features for Low-Light Image Enhancement

Abstract

To see clearly in low-light scenarios, a series of learning-based techniques have been developed to improve visual quality. However, due to the absence of semantic-level features, the existing methods are perhaps less effective on semantic-oriented visual analysis tasks (e.g., saliency detection). To break down the limitation, we propose a new classification-driven enhancement method with heterogeneous feature fusion. Specifically, we construct a new low-light image enhancement network by integrating features acquired from the pre-trained classification network. Then, to better exploit the semantic-level information, we establish a Heterogeneous Feature Fusion (HF2) operation with channel-and-spatial attention to strength the effects of cross-domain features. HF2 acts on not only the fusion between classification and encoded features but also the fusion between encoded and decoded features. Extensive experiments are conducted to indicate our superiority against other state-of-the-art methods. The application on saliency detection further reveals our effectiveness in settling the semantic-oriented visual tasks.