ArCSEM: Artistic Colorization of SEM Images via Gaussian Splatting (2410.21310v1)

Abstract: Scanning Electron Microscopes (SEMs) are widely renowned for their ability to analyze the surface structures of microscopic objects, offering the capability to capture highly detailed, yet only grayscale, images. To create more expressive and realistic illustrations, these images are typically manually colorized by an artist with the support of image editing software. This task becomes highly laborious when multiple images of a scanned object require colorization. We propose facilitating this process by using the underlying 3D structure of the microscopic scene to propagate the color information to all the captured images, from as little as one colorized view. We explore several scene representation techniques and achieve high-quality colorized novel view synthesis of a SEM scene. In contrast to prior work, there is no manual intervention or labelling involved in obtaining the 3D representation. This enables an artist to color a single or few views of a sequence and automatically retrieve a fully colored scene or video. Project page: https://ronly2460.github.io/ArCSEM

• The paper introduces ArCSEM, a two-stage method that uses Gaussian splatting to automatically reconstruct 3D scenes and propagate color across SEM images.
• The paper demonstrates enhanced global color consistency and reduced manual effort through pseudo-color supervision and template-based correspondence modules.
• The paper’s experiments show significant improvements over previous methods, underscoring its potential impact on scientific visualization and nanoscale image analysis.

Overview of "ArCSEM: Artistic Colorization of SEM Images via Gaussian Splatting"

This paper introduces ArCSEM, a method designed to facilitate the artistic colorization of Scanning Electron Microscope (SEM) images using Gaussian splatting. Scanning Electron Microscopes provide highly detailed grayscale images of microscopic objects, but the task of manually adding color to these images can be arduous. ArCSEM addresses this challenge by leveraging a single or a few colorized views to propagate color across multiple images of the microscopic scene. This is achieved through a partial 3D scene reconstruction which automatically extends the colored information to the rest of the dataset, thus significantly reducing the artist's workload while enhancing artistic freedom.

The authors propose a two-stage process combining grayscale 3D scene optimization with subsequent colorization. A key advantage of this approach is the automatic nature of the 3D reconstruction, which eliminates the need for manual intervention, making it more accessible for widespread artistic application. This is particularly relevant in scientific visualization where SEM images are prevalent and their colorization can aid in better understanding and presentation of nanoscale structures.

Methodology

ArCSEM employs 2D Gaussian Splatting (2DGS) for representing the SEM scene. This involves generating a 3D model from grayscale images, achieved without manually labeling the 3D features. Once the grayscale scene is modeled, artists provide several manually colorized SEM views, which are used for the colorization process. The methodology includes novel view synthesis and the application of affine color transformation to handle the variability in electron emission and scattering, which affects image illumination.

The paper also discusses methods of pseudo-color supervision based on projected color from a few artistically provided colored images, enhancing the transfer of color information across views. Moreover, a Template-based Correspondence Module and Coarse Color-Matching Loss are employed to retain consistency in color across the generated novel views, effectively addressing global color consistency issues and facilitating natural artistic output.

Results

The authors present comprehensive experimental results showcasing the effectiveness of ArCSEM. The system demonstrates significant improvement in the colorization process compared to pre-existing methods such as Plenoxels and earlier approaches not utilizing advanced Gaussian splatting techniques. Notably, ArCSEM maintains geometric and appearance consistency, even in high-resolution SEM images. The paper also includes ablation studies to underscore the contributions of each component in the proposed model, validating their necessity and impact.

Implications and Future Work

The implications of ArCSEM extend beyond the immediate task of colorizing SEM images. By automating part of the colorization process, this method enables more efficient and expressive use of SEM images in scientific analysis and illustration. The use of Gaussian splatting in the modeling of SEM images marks a meaningful intersection of computer graphics techniques and microscopic image analysis.

Given the demonstrated benefits, future exploration might focus on further refining the computational processes involved, perhaps exploring the integration of more advanced AI-driven segmentation techniques or incorporation with diffusion models to enhance predictive colorization for unseen areas in SEM images. Additionally, extending these techniques to a broader range of visualization tasks and applying them in fields such as biomedical imaging and nanofabrication could be highly beneficial. The collaborative potential of human AI co-creation, as highlighted in this paper, could lead to innovations in various domains requiring detailed artistic renderings from otherwise stark scientific instruments.