Model Parameter Learning for Real-Time High-Resolution Image Enhancement

Deep learning-based methods have achieved remarkable performance in image enhancement but generally require huge GPU memory, and computational cost when enhancing the high-resolution images. We explore to enhance high-resolution images like manual retouching by digital artists to obtain stable, and excellent performance in real-time. We extract the features from their thumbnails, and utilizes these features to guide the enhancement of the high-resolution images. Our designed light-weight convolution, and polynomial attention consume limited computational cost on the full-resolution image to retouch the image from both pixel-level, and global-level. Compared with the contemporary methods, our proposed method can work in real-time but surpass the state-of-the-art methods over 1.0 dB on the high-resolution image enhancement benchmark.