StarEnhancer: Learning Real-Time and Style-Aware Image Enhancement

Image enhancement is a subjective process whose targets vary with user preferences. In this paper, we propose a deep learning-based image enhancement method covering multiple tonal styles using only a single model dubbed StarEnhancer. It can transform an image from one tonal style to another, even if that style is unseen. With a simple one-time setting, users can customize the model to make the enhanced images more in line with their aesthetics. To make the method more practical, we propose a well-designed enhancer that can process a 4K-resolution image over 200 FPS but surpasses the contemporaneous single style image enhancement methods in terms of PSNR, SSIM, and LPIPS. Finally, our proposed enhancement method has good interactability, which allows the user to fine-tune the enhanced image using intuitive options.

Automated summary

In this paper, a deep learning-based image enhancement method called StarEnhancer is proposed to cover multiple tonal styles using a single model. Users can customize the model with a one-time setting to make the enhanced images more in line with their aesthetics. The method outperforms contemporary single-style image enhancement methods in terms of processing speed and quality metrics.