"Double-DIP": Unsupervised Image Decomposition via Coupled Deep-Image-Priors (1812.00467v2)

Abstract: Many seemingly unrelated computer vision tasks can be viewed as a special case of image decomposition into separate layers. For example, image segmentation (separation into foreground and background layers); transparent layer separation (into reflection and transmission layers); Image dehazing (separation into a clear image and a haze map), and more. In this paper we propose a unified framework for unsupervised layer decomposition of a single image, based on coupled "Deep-image-Prior" (DIP) networks. It was shown [Ulyanov et al] that the structure of a single DIP generator network is sufficient to capture the low-level statistics of a single image. We show that coupling multiple such DIPs provides a powerful tool for decomposing images into their basic components, for a wide variety of applications. This capability stems from the fact that the internal statistics of a mixture of layers is more complex than the statistics of each of its individual components. We show the power of this approach for Image-Dehazing, Fg/Bg Segmentation, Watermark-Removal, Transparency Separation in images and video, and more. These capabilities are achieved in a totally unsupervised way, with no training examples other than the input image/video itself.

• The paper proposes a novel unsupervised framework that harnesses coupled Deep-Image-Priors to decompose images into distinct layers for tasks like segmentation, dehazing, and watermark removal.
• It employs a multi-loss strategy—including reconstruction, exclusion, and regularization losses—to ensure accurate separation of image components.
• Experimental results highlight its effectiveness in handling non-uniform airlight and minimal data scenarios, marking a significant advancement in unsupervised deep learning for image processing.

Unsupervised Image Decomposition via Coupled Deep-Image-Priors: An Overview

The paper "Double-DIP: Unsupervised Image Decomposition via Coupled Deep-Image-Priors" introduces an innovative framework for image decomposition using unsupervised deep-learning techniques. This framework is particularly notable for its ability to tackle a wide range of computer vision tasks traditionally viewed as unrelated, including image segmentation, transparency separation, and dehazing, among others. The foundation of this methodology lies within the use of coupled Deep-Image-Prior (DIP) networks that facilitate the decomposition of an image into simpler constituent layers without reliance on extensive labeled datasets.

Key Concepts and Methodology

The primary strength of the Double-DIP framework is its ability to harness the internal statistics of images using twin DIP networks. Each network separately processes components that form the observed image, capitalizing on the notion that the internal statistics of a layer mixture are less complex compared to the composite whole. The fundamental hypothesis is built upon the recognition that visual data in separate layers exhibit higher self-similarity and lower entropy than when mixed, offering an easier generative task for each DIP network.

The Double-DIP framework is characterized by three main loss components to achieve effective layer decomposition:

1. Reconstruction Loss: Ensures that the recombined output layers approximate the input image closely.
2. Exclusion Loss: Minimizes correlation between the outputs of different DIPs, aiding in clearer separation of mixed components.
3. Regularization Loss: Task-specific constraints encourage meaningful learning of spatial masks or other relevant parameters.

Strong Results and Applications

The authors demonstrate the versatility of Double-DIP across several tasks, providing strong quantitative and qualitative results. Particularly in image dehazing, Double-DIP matches or surpasses performance benchmarks set by specialized methods. This performance aligns with the robust extraction of haze-free scenes and precise airlight estimation, even allowing for non-uniform airlight scenarios — a significant departure from most traditional assumptions.

Furthermore, experiments in watermark removal emphasize the framework's flexibility. With only a handful of images sharing a watermark, Double-DIP can effectively reconstruct clean images, indicating its potential utility in domains requiring minimal data reliance.

Implications and Future Directions

The theoretical underpinning of Double-DIP suggests profound implications both practically and theoretically. By obviating the need for ground-truth labeled datasets, it positions itself as a compelling tool for deploying vision systems in contexts where annotated data is scarce. Moreover, its applicability to a diverse set of tasks highlights the potential of its underlying philosophy in promoting new, unsupervised deep-learning methodologies in image processing domains.

The framework opens pathways for further research, particularly by integrating high-level semantic information or perceptual cues to enhance performance in tasks like semantic segmentation. Moreover, the model's ability to adaptively split layer information could inspire novel algorithms in data compression or efficient representation of complex scenes.

Through compelling empirical results and rigorous theoretical grounds, Double-DIP stands as a testament to the possibilities within unsupervised learning frameworks, suggesting promising advancements in the field of computer vision. The coupling of multiple unsupervised DIP networks and the exploration of their capabilities in real-world applications enrich the broader understanding of deep-learning strategies. This paper thus serves as a foundational reference for researchers exploring unsupervised techniques in dynamic and data-constrained environments.