An Image-to-video Model for Real-Time Video Enhancement

Recent years have witnessed the increasing popularity of learning-based methods to enhance the color and tone of images. Although these methods achieve satisfying performance on static images, it is non-trivial to extend such image-to-image methods to handle videos. A straight extension would easily lead to computation inefficiency or distracting flickering effects. In this paper, we propose a novel image-to-video model enforcing the temporal stability for real-time video enhancement, which is trained using only static images. Specifically, we first propose a lightweight image enhancer via learnable flexible 2-dimensional lookup tables (F2D LUTs), which can consider scenario information adaptively. To impose temporal constancy, we further propose to infer the motion fields via a virtual camera motion engine, which can be utilized to stabilize the image-to-video model with temporal consistency loss. Experimental results show that our image-to-video model not only achieves the state-of-the-art performance on the image enhancement task, but also performs favorably against baselines on the video enhancement task. Our source code is available at https://github.com/shedy-pub/I2VEnhance.