Underexposed Image Enhancement via Unsupervised Feature Attention Network

Abstract

In order to solve the problem that deep learning method needs a lot of paired data sets in image enhancement, this paper proposes unsupervised feature attention network (UFANet), which uses a new illumination estimation that combines pixel estimation and channel estimation to guide the network to decompose underexposed images. In addition, a feature attention residual network is trained to decompose under-exposed images into illumination and reflectance. Through a set of carefully designed non reference loss functions, which implicitly enhance the quality and drive the learning of the network, we train UFANet without any paired images. A large number of experiments on various benchmarks have proved the advantages of our method over the latest methods in terms of quality and quantity. Compared to the state-of-the-art methods, our method only needs to be trained on 350 underexposed images. All the above advantages make our UFANet attractive in practical applications.

End-to-End Retinex-Based Illumination Attention Low-Light Enhancement Network for Autonomous Driving at Night

“…The final enhanced image is the fusion between these two intermediate enhanced images. UFANet [ 40 ] adopts a feature attention network, combining pixel estimation and channel estimation to decompose low-light images into reflectance and illumination.…”