LuminanceGAN: Controlling the brightness of generated images for various night conditions.

There are diverse datasets available for training deep learning models utilized in autonomous driving. However, most of these datasets are composed of images obtained in day conditions, leading to a data imbalance issue when dealing with night condition images. Several day-to-night image translation models have been proposed to resolve the insufficiency of the night condition dataset, but these models often generate artifacts and cannot control the brightness of the generated image. In this study, we propose a LuminanceGAN, for controlling the brightness degree in night conditions to generate realistic night image outputs. The proposed novel Y-control loss converges the brightness degree of the output image to a specific luminance value. Furthermore, the implementation of the self-attention module effectively reduces artifacts in the generated images. Consequently, in qualitative comparisons, our model demonstrates superior performance in day-to-night image translation. Additionally, a quantitative evaluation was conducted using lane detection models, showing that our proposed method improves performance in night lane detection tasks. Moreover, the quality of the generated indoor dark images was assessed using an evaluation metric. It can be proven that our model generates images most similar to real dark images compared to other image translation models. [Display omitted] • Our novel Y-control loss is proposed to control the brightness degree of the generated night condition images. • We reduced the artifacts in the generated night condition images by incorporating a self-attention module. • Addressing data imbalance issues with varied brightness night images generated by our proposed model. • Our model exhibits a general applicability across various domains. [ABSTRACT FROM AUTHOR]