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?
- Master Video AI
- AI & Machine Learning
- Information Retrieval 101
- Embedding 101
- How to Pick the Right Vector Database for Your Use Case
- 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
Can Vision-Language Models generalize to new domains without retraining?
Vision-Language Models (VLMs) can exhibit some degree of generalization to new domains without the need for extensive re
How do relational databases manage concurrent access?
Relational databases manage concurrent access through a combination of locking mechanisms, isolation levels, and transac
How do we test and verify quantum algorithms in quantum programming languages?
Testing and verifying quantum algorithms in quantum programming languages involves several key steps. First, it is essen