Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
