WISE: Whitebox Image Stylization by Example-based Learning (2207.14606v1)

Abstract: Image-based artistic rendering can synthesize a variety of expressive styles using algorithmic image filtering. In contrast to deep learning-based methods, these heuristics-based filtering techniques can operate on high-resolution images, are interpretable, and can be parameterized according to various design aspects. However, adapting or extending these techniques to produce new styles is often a tedious and error-prone task that requires expert knowledge. We propose a new paradigm to alleviate this problem: implementing algorithmic image filtering techniques as differentiable operations that can learn parametrizations aligned to certain reference styles. To this end, we present WISE, an example-based image-processing system that can handle a multitude of stylization techniques, such as watercolor, oil or cartoon stylization, within a common framework. By training parameter prediction networks for global and local filter parameterizations, we can simultaneously adapt effects to reference styles and image content, e.g., to enhance facial features. Our method can be optimized in a style-transfer framework or learned in a generative-adversarial setting for image-to-image translation. We demonstrate that jointly training an XDoG filter and a CNN for postprocessing can achieve comparable results to a state-of-the-art GAN-based method.

• The paper introduces a novel differentiable framework that transforms traditional image filters into trainable operations.
• The paper employs Parameter Prediction Networks to accurately predict both global and local effect parameters for style reproduction.
• The paper demonstrates competitive performance relative to state-of-the-art neural style transfer methods while enabling interactive, high-resolution outputs.

Overview of "WISE: Whitebox Image Stylization by Example-based Learning"

The paper "WISE: Whitebox Image Stylization by Example-based Learning" presents a novel approach to artistic image processing that bridges the gap between traditional algorithmic stylization techniques and modern machine learning methods. The work focuses on the development of a system named WISE, which enables algorithmic image filters to be implemented as differentiable operations. This allows for the learning of effect parameters that can be adjusted to match specific reference styles. The system targets several image stylization techniques, including watercolor, oil, and cartoon effects, within a unified framework.

Methodology and Techniques

The cornerstone of the proposed framework is the transformation of algorithmic image processing operations into differentiable functions. This approach allows for the application of gradient descent techniques to optimize the parameters of the effects concerning a target style image. The paper focuses on adapting classical methods like the eXtended Difference-of-Gaussians (XDoG), cartoon stylization, and several others, turning them into differentiable pipelines using deep learning frameworks with auto-differentiation support like PyTorch.

Additionally, the paper develops Parameter Prediction Networks (PPNs) to predict both global and local parameters for these effects, effectively reversing the stylization process. The global PPNs help in approximating the stylization applied to an image, enabling efficient reproduction of styles, and thus, facilitating quick editing. For elements necessitating nuanced changes, local PPNs employ convolutional architectures to predict spatial parameter variations, enhancing the adaptability of the stylization based on content.

Results and Evaluations

The authors benchmark the system against state-of-the-art neural style transfer (NST) methods, such as STROTSS, showing competitive results. The differentiable framework supports interactive manipulation, offering significant flexibility compared to traditional NST methods. The paper validates its claims through various experiments, demonstrating the effectiveness of WISE in style adaptation and parameter prediction.

Moreover, the paper explores the potential for combining traditional techniques with modern generative methods. By incorporating CNN-based post-processing, the system successfully resolves the limitation of classical filters, which traditionally lack the ability to generate entirely new patterns outside their operational scope.

Implications and Future Directions

The implications of this research span both practical and theoretical aspects of AI-driven image processing. Practically, WISE enables high-resolution stylizations with lower computational cost, suitable for applications in real-time image editing and commercial software. Theoretically, it opens avenues for developing hybrid models that leverage both the efficiency of algorithmic methods and the flexibility of learning-based techniques.

Future developments in this vein could involve extending the framework to unpaired training regimes or exploring reinforcement learning aspects for further enhancing control over stylized outputs. Moreover, integrating stroke-based rendering approaches, though challenging, could further diversify the applicability of the WISE system.

In conclusion, WISE represents a significant step towards a more nuanced and adaptable approach to image stylization, providing a practical solution for high-quality artistic renditions with underlying interpretability and control.