AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-time Image Enhancement (2204.13983v1)

Abstract: The 3D Lookup Table (3D LUT) is a highly-efficient tool for real-time image enhancement tasks, which models a non-linear 3D color transform by sparsely sampling it into a discretized 3D lattice. Previous works have made efforts to learn image-adaptive output color values of LUTs for flexible enhancement but neglect the importance of sampling strategy. They adopt a sub-optimal uniform sampling point allocation, limiting the expressiveness of the learned LUTs since the (tri-)linear interpolation between uniform sampling points in the LUT transform might fail to model local non-linearities of the color transform. Focusing on this problem, we present AdaInt (Adaptive Intervals Learning), a novel mechanism to achieve a more flexible sampling point allocation by adaptively learning the non-uniform sampling intervals in the 3D color space. In this way, a 3D LUT can increase its capability by conducting dense sampling in color ranges requiring highly non-linear transforms and sparse sampling for near-linear transforms. The proposed AdaInt could be implemented as a compact and efficient plug-and-play module for a 3D LUT-based method. To enable the end-to-end learning of AdaInt, we design a novel differentiable operator called AiLUT-Transform (Adaptive Interval LUT Transform) to locate input colors in the non-uniform 3D LUT and provide gradients to the sampling intervals. Experiments demonstrate that methods equipped with AdaInt can achieve state-of-the-art performance on two public benchmark datasets with a negligible overhead increase. Our source code is available at https://github.com/ImCharlesY/AdaInt.

• The paper introduces AdaInt, an adaptive sampling mechanism that refines 3D LUT efficiency in handling non-linear image color transformations.
• The methodology employs a novel AiLUT-Transform operator that leverages CNN-predicted intervals with end-to-end training via binary search and interpolation.
• Experimental results on MIT-Adobe FiveK and PPR10K datasets demonstrate state-of-the-art performance with minimal computational overhead.

An Overview of AdaInt: Adaptive Intervals for Enhanced 3D LUT-Based Image Processing

The paper "AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-time Image Enhancement" introduces a novel approach to enhancing the capability and efficiency of 3D Lookup Tables (3D LUTs). This work is focused on image processing and proposes an adaptive interval learning mechanism aimed at overcoming limitations associated with traditional 3D LUTs in non-linear color transformations.

3D LUTs serve as an efficient tool for real-time image enhancement by modeling a non-linear 3D color transform. They discretize the color space into a lattice, upon which color transforms are interpolated linearly. However, conventional methodologies generally use uniform sampling, which becomes a bottleneck when addressing local non-linearities in the color transform, especially where dense sampling is needed.

Key Contributions

1. Adaptive Sampling Strategy: The paper introduces AdaInt (Adaptive Intervals Learning), a mechanism for adaptively learning non-uniform sampling intervals within the 3D LUT framework. The approach dynamically adjusts the density of sampling points based on the local non-linearity of the image transform requirements, allowing for dense sampling in areas of high non-linear changes, while sparing resources in near-linear regions.
2. Novel AiLUT-Transform Operator: The work presents AiLUT-Transform (Adaptive Interval LUT Transform), a differentiable lookup operator adapted to work with non-uniform sampling. It incorporates both binary search and interpolation, permitting end-to-end training by providing gradients to sampling intervals.
3. Demonstrated Efficiency and Efficacy: The approach achieves state-of-the-art performance on established public datasets in image enhancement tasks with minimal computational overhead, owing to the efficient use of adaptive sampling.

Methodological Approach

The proposed AdaInt module integrates into existing 3D LUT frameworks by refining the manner sampling points are allocated across the lattice. A convolutional neural network (CNN) predicts not only the output color values but also the sampling coordinates based on the image content. By reparameterizing sampling intervals and leveraging end-to-end training through AiLUT-Transform, the system learns more effective sampling strategies without excessively increasing computational costs. The improved flexibility made possible by AdaInt broadens the expressiveness of 3D LUTs, making them better suited for varying image content and conditions.

Experiments and Results

The effectiveness of AdaInt was tested on two major public datasets, MIT-Adobe FiveK and PPR10K, in applications such as photo retouching and tone mapping. The experimental results evidenced the superior performance of the proposed approach over traditional and contemporary 3D LUT methods in terms of both quantitative metrics (e.g., PSNR, SSIM, ΔE_ab) and qualitative visual assessments.

Implications and Future Work

This research bears several implications for both theoretical and practical aspects of image processing. The adaptive mechanism offered by AdaInt could be generalized beyond image enhancement tasks, serving as a foundation for further explorations into adaptive sampling techniques in other domains where 3D LUTs or similar structures are applicable. Given that the method holds to efficient execution on high-resolution images while maintaining superior performance, it underscores the feasibility of deploying such tools in real-time embedded systems and mobile applications.

Future work may involve extending the adaptability of AdaInt to incorporate spatial awareness and noise robustness, pushing the boundaries of what can be achieved with 3D LUTs in processing complex scenes.

In conclusion, the paper provides a comprehensive and technically robust solution to a prominent challenge in real-time image enhancement, marking a notable advancement in the application of neural networks and adaptive techniques to traditional model aspects in computer vision.