What is a patch in image processing?

A patch in image processing refers to a small, localized region or subset of pixels within a larger image. Patches are typically rectangular or square-shaped areas extracted to analyze or process specific features, textures, or patterns. They serve as a fundamental building block for many algorithms, allowing developers to focus computational resources on relevant parts of an image rather than processing the entire dataset at once. For example, in convolutional neural networks (CNNs), patches are used to apply filters that detect edges, shapes, or textures by sliding over the image. This localized approach reduces computational complexity and enables algorithms to learn hierarchical features effectively.

Patches are widely used in tasks like object detection, image inpainting, or texture synthesis. In object detection, a sliding window technique might extract patches to identify whether a specific object (e.g., a face) exists within that region. Similarly, in medical imaging, patches can help isolate anomalies like tumors by analyzing small regions of interest. Another example is image stitching, where overlapping patches from multiple images are matched to create panoramas. Patches also play a role in compression algorithms like JPEG, which divides images into 8x8 pixel blocks (patches) to apply discrete cosine transforms, reducing file size while preserving visual quality.

When working with patches, developers must consider factors like patch size, stride (the step size when sliding the window), and overlap. A smaller patch size captures fine details but may miss broader context, while larger patches retain more spatial information at the cost of increased computation. Overlapping patches can mitigate edge artifacts in tasks like denoising or super-resolution. For instance, in training a CNN for image classification, non-overlapping patches with a stride equal to the patch size reduce redundancy, whereas overlapping patches in semantic segmentation help ensure smoother predictions. Tools like Python’s NumPy or OpenCV provide functions to efficiently extract and process patches, enabling developers to balance accuracy and performance.