TITLE: A Retinex based GAN Pipeline to Utilize Paired and Unpaired Datasets for Enhancing Low Light Images 

ABSTRACT: Low light image enhancement is an important challenge for the development of
robust computer vision algorithms. The machine learning approaches to this have
been either unsupervised, supervised based on paired dataset or supervised
based on unpaired dataset. This paper presents a novel deep learning pipeline
that can learn from both paired and unpaired datasets. Convolution Neural
Networks (CNNs) that are optimized to minimize standard loss, and Generative
Adversarial Networks (GANs) that are optimized to minimize the adversarial loss
are used to achieve different steps of the low light image enhancement process.
Cycle consistency loss and a patched discriminator are utilized to further
improve the performance. The paper also analyses the functionality and the
performance of different components, hidden layers, and the entire pipeline.
Low light image enhancement is an important challenge for the development of
robust computer vision algorithms. The machine learning approaches to this have
been either unsupervised, supervised based on paired dataset or supervised
based on unpaired dataset. This paper presents a novel deep learning pipeline
that can learn from both paired and unpaired datasets. Convolution Neural
Networks (CNNs) that are optimized to minimize standard loss, and Generative
Adversarial Networks (GANs) that are optimized to minimize the adversarial loss
are used to achieve different steps of the low light image enhancement process.
Cycle consistency loss and a patched discriminator are utilized to further
improve the performance. The paper also analyses the functionality and the
performance of different components, hidden layers, and the entire pipeline.
A Retinex based GAN Pipeline to Utilize Paired and Unpaired Datasets for Enhancing Low Light Images
http://arxiv.org/abs/2006.15304v2