Abstract: A deep learning technique for the super-resolution of a single image. With our approach, spatial dependencies are captured and end-to-end mapping between the low/high-resolution images is learned.

Because their convolutional layers have fewer parameters compared with the fully connected layers of a traditional neural network, CNNs perform more efficiently on image processing tasks. CNNs use a technique known as parameter sharing that makes them much more efficient at handling image data.

Image classification using Convolutional Neural Networks (CNN) has revolutionized computer vision tasks by enabling automated and accurate recognition of objects within images. Consequently, this technology has significantly advanced fields such as medical imaging, autonomous driving, and industrial automation.

What is CNN in image processing? A. CNN stands for Convolutional Neural Network and is a type of deep learning algorithm used for analyzing and processing images. It performs a series of mathematical operations such as convolutions and pooling on an image to extract relevant features.