LightenNet: A Convolutional Neural Network for weakly illuminated image enhancement

• Published in: Pattern recognition letters Vol. 104; pp. 15 - 22
• Main Authors: Li, Chongyi, Guo, Jichang, Porikli, Fatih, Pang, Yanwei
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.03.2018; Elsevier Science Ltd
• Subjects: Artificial neural networks; CNNs; Computer vision; Illumination; Image degradation; Image enhancement; Image processing systems; Image quality; Light; Low light image enhancement; Mathematical models; Neural networks; Qualitative research; Quality assessment; Quality control; Retinex (algorithm); Weak illumination image enhancement; Weak illumination image enhancement; Low light image enhancement; 41A10; 65D05; 65D17; Image degradation; 41A05; CNNs
• ISSN: 0167-8655; 1872-7344
• DOI: 10.1016/j.patrec.2018.01.010

•We propose a trainable CNN for weakly illuminated image enhancement.•We propose a Retinex model-based weakly illuminated image synthesis approach.•The proposed method generalizes well to diverse weakly illuminated images. Weak illumination or low light image enhancement as pre-processing is needed in many computer vision tasks. Existing methods show limitations when they are used to enhance weakly illuminated images, especially for the images captured under diverse illumination circumstances. In this letter, we propose a trainable Convolutional Neural Network (CNN) for weakly illuminated image enhancement, namely LightenNet, which takes a weakly illuminated image as input and outputs its illumination map that is subsequently used to obtain the enhanced image based on Retinex model. The proposed method produces visually pleasing results without over or under-enhanced regions. Qualitative and quantitative comparisons are conducted to evaluate the performance of the proposed method. The experimental results demonstrate that the proposed method achieves superior performance than existing methods. Additionally, we propose a new weakly illuminated image synthesis approach, which can be use as a guide for weakly illuminated image enhancement networks training and full-reference image quality assessment.