Interactive PDI: A Tool for Study and Experimentation with Spatial and Frequency Approach

• João Lucas Mayrinck UFV

DOI: https://doi.org/10.5753/wsis.2025.15098

Abstract

This article presents an interactive system for digital image processing (DIP), focusing on the application and analysis of spatial and frequency domain filters. The system, developed in Python (Streamlit) with a complementary web interface in HTML/JavaScript, aims to serve as an educational and research tool. It allows image manipulation using low-pass filtersand high-pass filters, as well as frequency domain operations via the Fourier Transform. The system also includes features for adding and removing various types of noise, segmentation, and morphological operations. The results demonstrate the tool’s effectiveness in visualizing filter effects and understanding DIP concepts, although it presents performance limitations with high-resolution images and lacks automatic quantitative metrics. The work concludes by highlighting the relevance of the tool for teaching and research in DIP, suggesting future improvements such as the inclusion of quality metrics, support for multiple formats, and integration with machine learning.

Keywords: Digital Image Processing, Spatial Filters, Frequency Domain, Educational Tool, Morphological Operations, Image Segmentation

References

Gonzalez, R. C., & Woods, R. E. (2018). Digital Image Processing (4th ed.). Pearson.

Marques, O. (2014). Processamento Digital de Imagens: Uma Abordagem Prática com MATLAB. Brasport.

Pedrini, H., & Schwartz, W. R. (2008). Análise de Imagens Digitais: Princípios, Algoritmos e Aplicações. Cengage Learning.

Forsyth, D. A., & Ponce, J. (2011). Computer Vision: A Modern Approach (2nd ed.). Pearson.

Lima, L. C. et al. (2015). Verificação de modelos aplicada aos filtros espaciais em processamento de imagens. ENCOSIS.

Gonçalves, L. A. (2004). Um estudo sobre a Transformada Rápida de Fourier e seu uso em processamento de imagens. Universidade Federal do Rio Grande do Sul.

Benício, B. G. S. (2022). Uma ferramenta didática para ensino de filtragem no domínio da frequência. Universidade Federal do Rio Grande do Norte.

Schneider, C. A., Rasband, W. S., & Eliceiri, K. W. (2012). NIH Image to ImageJ: 25 years of image analysis. Nature Methods, 9(7), 671–675.

Bradski, G. (2000). The OpenCV Library. Dr. Dobb’s Journal of Software Tools.

Eddins, S. L. (2004). Digital Image Processing Using MATLAB. MathWorks & Gatesmark Publishing.