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Dataset of scattered images using noncoherent light under varying diffusion conditions and projected patterns
Roger Chiu-Coutino1,2, Miguel S Soriano-Garcia3, Carlos Israel Medel-Ruiz1
1Departamento de ciencias exactas y tecnología, Centro universitario de los Lagos, Universidad de Guadalajara, Enrique Díaz de León 1144, Lagos de Moreno, 47463, Jalisco, México.
Data in Brief
|February 20, 2026
Summary
This study introduces a new dataset of scattered images captured with a low-cost Raspberry Pi optical system. This data aids deep learning in optics for image restoration in complex scattering environments.
Area of Science:
- Optics and Photonics
- Computer Vision
- Machine Learning
Background:
- Scattering of light poses challenges in optical imaging and inverse problems.
- Developing robust image restoration models requires diverse and complex datasets.
- Low-cost, accessible optical systems are needed for wider research participation.
Purpose of the Study:
- To present a novel experimental dataset of scattered images.
- To support research in deep learning for inverse problems in optics.
- To facilitate the training and benchmarking of image restoration models.
Main Methods:
- Utilized a Raspberry Pi-based optical system for image acquisition.
- Projected diverse patterns through various optical diffusers with differing scattering properties.
- Captured pairs of scattered images and original patterns (ground truth) at 256x256 resolution.
Main Results:
- Generated a dataset featuring geometric shapes, digits, and textures.
- The dataset encompasses varied scattering regimes and optical complexities.
- Provided ground truth data for evaluating image recovery algorithms.
Conclusions:
- The dataset is valuable for advancing deep learning in optical inverse problems.
- Enables the development and testing of image restoration models for scattering media.
- Promotes research in low-cost optical imaging systems.
