Controllable Low-Light Image Enhancement with Restoration Level Estimator

• Gabriel Tadiello UFRGS
• Bernardo Henz IFFar
• Manuel M. Oliveira UFRGS

Resumo

Low-light image enhancement (LLIE) is an important task in image processing and computer vision, addressing the need to improve the visual quality of images captured in suboptimal lighting conditions. Enhanced images are not only more visually appealing but also more effective for downstream tasks such as object detection and classification. However, while the desired level of enhancement is often subjective and varies across users and applications, previous LLIE techniques do not allow users to control the desired level of enhancement intensity. We introduce the Restoration Level Estimator (RLE) block, a novel component designed to provide control over the enhancement level in existing LLIE models. The RLE block can be seamlessly integrated into convolutional neural networks, adding a new channel that allows users to adjust the level of enhancement applied to input images. Our experiments show that, in addition to offering control, the RLE block can improve the overall performance of LLIE models as measured by PSNR, SSIM, and LPIPS. We demonstrate the flexibility of our approach across multiple LLIE models, highlighting its potential to improve both user experience and model performance.

Palavras-chave: Visualization, Computer vision, Lighting, Object detection, User experience, Image restoration, Convolutional neural networks, Image enhancement