How is image processing and computer vision related?

Image processing and computer vision are closely related fields that overlap in techniques and goals but differ in scope. Image processing focuses on manipulating images to improve quality, extract details, or transform data—like adjusting contrast or reducing noise. Computer vision (CV) uses those processed images to derive higher-level understanding, such as identifying objects or interpreting scenes. Think of image processing as preparing the raw material (images) and CV as analyzing that material to make decisions. For example, before a CV system detects faces in a photo, it might first apply image processing steps like sharpening edges or normalizing lighting.

While image processing operates at the pixel level, computer vision works with semantic features. Image processing techniques, such as Gaussian blur for noise reduction or histogram equalization for contrast adjustment, modify pixel values directly. These steps are often prerequisites for CV tasks. For instance, edge detection algorithms like Canny or Sobel filters (image processing) highlight boundaries in an image, which a CV system could then use to recognize shapes or track objects. The distinction lies in intent: image processing aims to enhance or transform images, while CV seeks to extract meaning from them. A developer might use OpenCV’s image filtering functions (processing) before feeding data into a YOLO model (CV) for object detection.

Real-world applications often combine both. In medical imaging, image processing algorithms might sharpen MRI scans or remove artifacts, enabling CV models to segment tumors more accurately. Autonomous vehicles use image processing to correct lens distortion or adjust exposure in real-time camera feeds; CV algorithms then identify pedestrians or traffic signs from the cleaned data. Even basic tasks like QR code scanning rely on this interplay: image processing corrects perspective distortion and binarizes the image, while CV decodes the pattern into meaningful data. Understanding both fields helps developers design efficient pipelines, as optimizing image quality early can drastically improve the accuracy of downstream CV tasks.