In image processing, a patch refers to a small, localized section or subset of an image. It is often extracted from a larger image to analyze specific features or conduct operations like filtering, texture analysis, or object recognition on that smaller region. A patch can be as simple as a rectangular or square block of pixels, usually with a fixed size, that helps focus attention on a part of the image while ignoring irrelevant areas. For example, in convolutional neural networks (CNNs), patches are used in the convolutional layer where filters or kernels are applied to scan through the image, extracting local features such as edges or textures. In image registration, patches can also be used to match corresponding points in two different images of the same scene. Additionally, patch-based methods are widely used in applications like image denoising, super-resolution, and segmentation, where each patch is processed to improve image quality or extract detailed information about structures within the image. The advantage of working with patches is that it reduces the computational complexity by focusing on small regions of interest instead of processing the entire image at once.
What is a patch in image processing?

- Getting Started with Zilliz Cloud
- Exploring Vector Database Use Cases
- Natural Language Processing (NLP) Basics
- The Definitive Guide to Building RAG Apps with LangChain
- Accelerated Vector Search
- 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
What are state-space models in time series analysis?
State-space models are a powerful framework used in time series analysis to represent dynamic systems. At their core, th
How does the MIT license work?
The MIT License is a permissive open-source license that allows developers to freely use, modify, and distribute softwar
How does AutoML manage data augmentation for image tasks?
AutoML manages data augmentation for image tasks by automating the process of generating additional training data in a w