Why are CNNs particularly well-suited for image recognition tasks?

Convolutional Neural Networks (CNNs) are specifically designed to handle the spatial structure of images, which is why they are particularly effective for image recognition tasks. The ability of CNNs to capture spatial hierarchies means that they can detect patterns at various levels of abstraction. For instance, they can identify edges in lower layers, textures and shapes in intermediate layers, and more complex objects in deeper layers of the network. This hierarchical feature extraction allows CNNs to learn and generalize better from images compared to other types of neural networks that do not use this structure.

The other options do not accurately represent the strengths of CNNs. Processing images in one-dimensional arrays is not a characteristic of CNNs; in fact, they operate on multi-dimensional arrays (e.g., height, width, and depth of images). While CNNs can work with both grayscale and color images, they do not operate exclusively on grayscale images, which limits their application. Finally, while CNNs can be more efficient in terms of the amount of data they require to learn effectively, they do not inherently require less data than other models universally. The efficiency derived from their architecture in learning from spatial hierarchies is a key reason for their success in image recognition tasks.