Edge Boosted Global Awared Low-light Image Enhancement Network

Authors : Büşra Söylemez, Serdar Çiftçi

Pages : 107-117

Doi:10.24012/dumf.1395168

View : 89 | Download : 162

Publication Date : 2024-03-29

Article Type : Research Paper

Abstract :Low-light images are captured in situations where the lighting is poor or the camera hardware is not capable of producing good quality images. These types of images tend to have low contrast, blurry details, noise, and color distortion. In computer vision applications, image brightness plays a crucial role, and therefore, low-light image enhancement is used as a preprocessing step. In this study, we have improved the Low-Light Enhancement Network with Global Awareness (GLADNet) method by adding a UNet-based edge information extraction unit. The channel attention mechanism was also incorporated into the edge information extraction unit to achieve color preservation. Our experiments show that our proposed method has achieved higher PSNR, SSIM, and FSIM metrics compared to reference images. Additionally, it has produced lower NIQE and BRISQUE values for non-reference performance evaluation. Moreover, our proposed method removes noise better and produces visual results that are closer to the target images.
Keywords : Düşük Aydınlıklı Görüntü İyileştirme, U net Tabanlı Kenar Çıkarımı, Kanal Dikkat, Renk Korunumu, Geliştirilmiş GladNET, Canny