Is deep learning killing image processing/computer vision?

No, deep learning is not killing image processing or computer vision. Instead, it has become a powerful tool that complements traditional methods. While deep learning dominates tasks like image classification, object detection, and segmentation, many foundational image processing techniques remain essential. For example, edge detection (e.g., Sobel or Canny filters), histogram equalization, and morphological operations are still widely used in preprocessing, post-processing, or scenarios where interpretability and low computational costs matter. Deep learning excels at learning complex patterns from data, but it doesn’t replace the need for basic algorithms in resource-constrained environments or applications requiring precise control over outputs.

Deep learning and traditional methods often work together. For instance, a pipeline might use classical techniques to preprocess images (e.g., noise reduction with Gaussian blur) before feeding them into a neural network. Similarly, traditional computer vision algorithms like optical flow or feature matching (e.g., SIFT) are still used in robotics and augmented reality, where real-time performance or sparse data is critical. OpenCV, a library built on classical methods, remains a cornerstone for developers, even as frameworks like PyTorch or TensorFlow handle deep learning tasks. The two approaches solve different problems: deep learning handles high-dimensional, data-rich tasks, while traditional methods provide efficiency and transparency.

The evolution of computer vision is more about integration than replacement. For example, autonomous vehicles combine convolutional neural networks (CNNs) for object detection with traditional algorithms like Kalman filters for tracking motion. Medical imaging uses CNNs to identify tumors but relies on thresholding or region-growing techniques for precise segmentation. Developers often choose between methods based on constraints like dataset size, hardware limitations, or the need for explainability. While deep learning has expanded what’s possible, it hasn’t rendered classical techniques obsolete—it has simply added new tools to the toolbox. The field now requires proficiency in both domains to build robust, efficient systems.