Image Enhancement of Jewelry Data Using CycleGAN

Abstract

Since jewelry images are characterized by light reflections, they require more elaborate corrections than landscape images. In this study, we develop a system that uses CycleGAN to learn the translation between the captured image and the image processed by an expert using Photoshop. We applied various low-light image correction methods to the jewelry and compared them using the evaluation metrics of PSNR, SSIM, and color similarity in LAB color space. The results show that CycleGAN outperforms the other methods with the help of image registration technique.