Learning Image-adaptive 3D Lookup Tables for High Performance Photo Enhancement in Real-time (2009.14468v1)

Abstract: Recent years have witnessed the increasing popularity of learning based methods to enhance the color and tone of photos. However, many existing photo enhancement methods either deliver unsatisfactory results or consume too much computational and memory resources, hindering their application to high-resolution images (usually with more than 12 megapixels) in practice. In this paper, we learn image-adaptive 3-dimensional lookup tables (3D LUTs) to achieve fast and robust photo enhancement. 3D LUTs are widely used for manipulating color and tone of photos, but they are usually manually tuned and fixed in camera imaging pipeline or photo editing tools. We, for the first time to our best knowledge, propose to learn 3D LUTs from annotated data using pairwise or unpaired learning. More importantly, our learned 3D LUT is image-adaptive for flexible photo enhancement. We learn multiple basis 3D LUTs and a small convolutional neural network (CNN) simultaneously in an end-to-end manner. The small CNN works on the down-sampled version of the input image to predict content-dependent weights to fuse the multiple basis 3D LUTs into an image-adaptive one, which is employed to transform the color and tone of source images efficiently. Our model contains less than 600K parameters and takes less than 2 ms to process an image of 4K resolution using one Titan RTX GPU. While being highly efficient, our model also outperforms the state-of-the-art photo enhancement methods by a large margin in terms of PSNR, SSIM and a color difference metric on two publically available benchmark datasets.

• The paper introduces a novel method combining learned image-adaptive 3D LUTs with a compact CNN for robust real-time photo enhancement.
• The approach achieves superior PSNR and SSIM scores while processing 4K images in under 2 milliseconds with a minimal model footprint.
• It overcomes traditional fixed LUT limitations by dynamically adapting to diverse image contexts, enabling efficient and high-quality enhancements.

Learning Image-Adaptive 3D Lookup Tables for High Performance Photo Enhancement in Real-Time

Photo enhancement has become an intrinsic component within the field of computer vision and digital photography, driven by the imperative to improve image quality captured under diverse conditions. This paper introduces a novel approach to automatic photo enhancement through the learning of image-adaptive 3D Lookup Tables (3D LUTs), which promises to deliver high efficiency and superior results.

The proposed methodology synergizes multiple basis 3D LUTs with a small convolutional neural network (CNN) to automatically curate an image-adaptive 3D LUT that enhances photo quality. Traditional 3D LUTs, whilst efficacious in color manipulation, are predominantly manually predefined and lack the flexibility to adapt to varying image contexts. Here, the 3D LUTs are dynamically derived through learning mechanisms, thus overcoming significant limitations of conventional fixed LUTs and offering enhanced adaptability and performance in real-time settings.

The 3D LUT approach renders high-quality transformations due to the powerful combination of these flexible LUTs and the auxiliary CNN, which operates on down-sampled image versions to identify content-dependent transformation weights. This amalgamation facilitates robust photo enhancement while preserving computational resources, boasting a model footprint of less than 600K parameters and impressive processing speeds of less than 2 milliseconds for 4K resolution images when executed on a Titan RTX GPU.

From a numerical standpoint, the proposed model outstrips current state-of-the-art techniques in both PSNR and SSIM metrics by a considerable margin across benchmark datasets. Such performance underscores its efficacy in delivering crisp and visually appealing outputs, positioning it as a valuable tool in both consumer and professional applications.

Theoretical implications of this model extend into the broader field of image processing, where iterative improvement of learning techniques could be informed by similar strategies of pairing neural network capabilities with non-parametric models like LUTs. Practical implications are effectively demonstrated in high-resolution imaging applications, particularly where computational efficiency is paramount.

As the field advances, one potential avenue for research could involve integrating the adaptive 3D LUT approach with local enhancement strategies to further handle high dynamic range scenes or mitigate noise artifacts in low-light scenarios. Additionally, expanding this framework to encompass advancements in other image modalities, such as depth or multispectral imaging, could extend the model's applicability.

This work marks a significant contribution to the domain of automated image enhancement by introducing an approach that is simultaneously efficient, adaptable, and qualitatively superior. Future endeavors in the AI landscape might explore refining adaptive LUT strategies to encompass even more sophisticated and contextually aware enhancements, thereby broadening the utility and robustness of photo enhancement technologies.