Specularity Factorization for Low-Light Enhancement (2404.01998v1)

Abstract: We present a new additive image factorization technique that treats images to be composed of multiple latent specular components which can be simply estimated recursively by modulating the sparsity during decomposition. Our model-driven {\em RSFNet} estimates these factors by unrolling the optimization into network layers requiring only a few scalars to be learned. The resultant factors are interpretable by design and can be fused for different image enhancement tasks via a network or combined directly by the user in a controllable fashion. Based on RSFNet, we detail a zero-reference Low Light Enhancement (LLE) application trained without paired or unpaired supervision. Our system improves the state-of-the-art performance on standard benchmarks and achieves better generalization on multiple other datasets. We also integrate our factors with other task specific fusion networks for applications like deraining, deblurring and dehazing with negligible overhead thereby highlighting the multi-domain and multi-task generalizability of our proposed RSFNet. The code and data is released for reproducibility on the project homepage.

• The paper introduces RSFNet, a zero-reference network that recursively factors latent specular components for efficient low-light enhancement.
• The method unrolls optimization steps into interpretable network layers, achieving robust performance across diverse image enhancement tasks.
• RSFNet demonstrates superior generalization on benchmarks without requiring paired training data, surpassing traditional LLE methods.

Recurrent Specularity Factorization Network for Enhanced Low-Light Images

Introduction

Low-Light Enhancement (LLE) is a critical pre-processing step for various computer vision applications that suffer from images captured under insufficient lighting conditions. Traditional LLE methods rely on manually-designed optimization strategies which do not necessarily capture all nuances of image degradation in low-light conditions. On the other hand, data-driven approaches, while effective, often lack interpretability and may not generalize well across different lighting conditions due to their reliance on extensive labeled data. This paper introduces a novel approach to LLE through Recursive Specularity Factorization (RSF), which decomposes images into multiple latent specular components, enabling efficient low-light image enhancement and supporting a variety of enhancement tasks including dehazing, deraining, and deblurring.

Recursive Specularity Factorization Network (RSFNet)

At the heart of the proposed method lies the RSFNet, a model-driven network that recursively decomposes an input image into latent specular factors. RSFNet operationalizes a new additive image factorization technique, leveraging the concept that images consist of multiple latent specular components separable through sparsity modulation. By unrolling the optimization steps into a sequence of network layers, the RSFNet accurately estimates these factors using a minimal set of parameters, making it notably efficient. The factors, by design, are interpretable and can be easily manipulated or fused for various image enhancement tasks.

Zero-Reference Low Light Enhancement

A significant advantage of the proposed method is its ability to operate in a zero-reference setting. The RSFNet does not rely on paired or unpaired supervision, making it robust across different datasets without the need for extensive training data. This quality is essential for real-world applicability where acquiring labeled data is often impractical. The paper demonstrates that RSFNet outperforms existing state-of-the-art LLE methods on standard benchmarks while ensuring better generalization across multiple datasets.

Implications and Theoretical Significance

The introduction of RSFNet marks a significant advancement in the way images are factorized for enhancement tasks. Unlike previous approaches that rely on fixed-factor models or intensity-based decompositions, RSF provides a more flexible and robust framework for understanding and manipulating the underlying components of low-light images. Theoretically, RSF's ability to generalize without the need for extensive labeled data challenges the prevailing trend in deep learning that often equates data quantity with performance. Practically, the method’s efficiency and versatility hold promise for integration into existing image processing pipelines, enhancing the performance of downstream tasks that are sensitive to lighting conditions.

Future Directions in Generative AI

The RSFNet introduces a promising direction for future research in generative AI, especially in the context of interpretable machine learning models. One potential area for further exploration is the application of RSFNet in tasks beyond LLE, such as image segmentation, object detection in low-light conditions, and even non-visual signal processing tasks where similar factorization principles could apply. Additionally, extending the RSFNet to handle dynamic scenes captured in videos presents an exciting challenge, potentially opening up new avenues for low-light video enhancement and analysis.

Conclusion

The Recursive Specularity Factorization Network offers a novel and effective solution to Low-Light Enhancement, combining the interpretability of model-based methods with the performance advantages of data-driven approaches. By decomposing images into interpretable specular components, RSFNet not only achieves superior enhancement results but also provides a versatile framework for a range of image processing tasks. As generative AI continues to evolve, the principles underpinning RSFNet could inspire new models that balance performance with interpretability and generalizability, pushing the boundaries of what's possible in low-light image processing and beyond.