Image preprocessing is required to enhance the quality of input data and ensure consistency for machine learning models. Preprocessing steps like resizing, normalization, and noise reduction improve the model’s ability to learn meaningful patterns. For instance, resizing standardizes image dimensions, while normalization scales pixel values to a uniform range, preventing numerical instability during training. Removing noise and applying filters help focus on relevant features, improving accuracy. Preprocessing ensures that the input data is clean, uniform, and optimized for reliable and efficient model performance.
Why is image preprocessing required?

- Natural Language Processing (NLP) Basics
- Natural Language Processing (NLP) Advanced Guide
- How to Pick the Right Vector Database for Your Use Case
- Evaluating Your RAG Applications: Methods and Metrics
- Exploring Vector Database Use Cases
- All learn series →
Recommended AI Learn Series
VectorDB for GenAI Apps
Zilliz Cloud is a managed vector database perfect for building GenAI applications.
Try Zilliz Cloud for FreeKeep Reading
How do cultural differences influence AR user experience design?
Cultural differences significantly impact Augmented Reality (AR) user experience design by shaping how users interact wi
How do guardrails work in LLMs?
Guardrails in LLMs work through a combination of techniques to guide model behavior and output. These include fine-tunin
How are embeddings used in document retrieval?
Embeddings are a crucial technique used in document retrieval systems to represent text data in a way that makes it easi