LLE-NET: A Low-Light Image Enhancement Algorithm Based on Curve Estimation

Abstract

Low-light image enhancement is very significant for vision tasks. We introduce Low-light Image Enhancement via Deep Learning Network (LLE-NET), which employs a deep network to estimate curve parameters. Cubic curves and gamma correction are employed for enhancing low-light images. Our research trains a lightweight network to estimate the parameters that determine the correction curve. By the results of the deep learning network, accurate correction curves are confirmed, which are used for the per-pixel correction of RGB channels. The image enhanced by our models closely resembles the input image. To further accelerate the inferring speed of the low-light enhancement model, a low-light enhancement model based on gamma correction is proposed with one iteration. LLE-NET exhibits remarkable inference speed, achieving 400 fps on a single GPU for images sized 640×480×3 while maintaining pleasing enhancement quality. The enhancement model based on gamma correction attains an impressive inference speed of 800 fps for images sized 640×480×3 on a single GPU.

Keywords

• low-light image enhancement
• curve enhancement
• zero-reference learning