Author: Zen and the Art of Computer Programming
1 Introduction
Image processing is an important branch of computer vision. In modern applications, deep learning techniques are applied to tasks such as image classification, object detection, image registration, image super-resolution, and image generation. Convolutional Neural Networks (CNN for short), a deep learning model, is a very effective technique in the field of image processing. It can automatically extract the features in the image, and further use these features for classification, recognition, retrieval and reconstruction operations. This article will give a comprehensive and systematic introduction to the CNN model and its related concepts, and expounds the structure, characteristics, applications, advantages and disadvantages, computational efficiency, training technology and other aspects of CNN. By reading this article, readers can better understand CNN, master its usage and technology. In addition, this article will also provide corresponding code examples for readers' reference.
2. Core concepts
2.1 Image data
The image is a two-dimensional matrix, each element represents the intensity value of a pixel. Generally speaking, an image is composed of several pixels, and each pixel has a coordinate position (x, y) and a color value. The coordinate position determines the position of the pixel in the image, and the color value represents the gray level or color information of the pixel. Image data includes three aspects: one is the original image; the other is the image obtained after preprocessing; the third is the training set, verification set and test set. The training set is used to train the parameters of the model, the validation set is used to evaluate the performance of the model, and the test set is used to finally determine the effect of the model.
2.2 Receptive field
In the CNN model, the receptive field of the feature map refers to a sub-region of the input image, which can receive the input signal of the neurons of the previous layer and pass it to the neurons of the current layer. By adjusting the size of the receptive field, the receptive range of the CNN model can be changed, thereby improving the accuracy and robustness of the model. Usually, a larger receptive field can capture more feature information of the surrounding area, but it also increases the amount of calculation and parameters, which is why some researchers propose multi-scale