In modern industrial production, accurate and efficient detection of strip-steel surface defects is critical for maintaining product quality and minimizing economic losses. With the rapid advancement ...

Convolution operations are fundamental in image processing, signal processing, and deep learning. They are used to apply filters to images, detect edges, and perform various other tasks. NumPy ...

This project involves implementing the forward pass of an 18-layer Convolutional Neural Network (CNN) in MATLAB for object detection. The goal is to classify 32x32x3 images into one of ten categories, ...

A website called 'Animated AI' has been published that uses animation to explain 'Convolutional Neural Networks (CNN),' a technology widely used in the field of machine learning. The website visually ...

Abstract: In natural images, information is conveyed at different frequencies where higher frequencies are usually encoded with fine details and lower frequencies are usually encoded with global ...

Convolutional neural networks are an important category of deep learning, currently facing the limitations of electrical frequency and memory access time in massive data processing. Optical computing ...

Abstract: Deep convolutional neural networks have achieved remarkable progress in recent years. However, the large volume of intermediate results generated during inference poses a significant ...

Most often we won't be implementing convolution every time we need to use it. Therefore, it is important to know functions from numerical software libraries we can use. Before we dive into the ...

Lucy-Richardson deconvolution is an iterative algorithm for recovering an image which is blurred by a known point spread function (PSF). You can find the iterative algorithm steps in Wikipedia.

There are quite a few software applications that are considered indispensable for a variety of specialized subject areas of academia and research, such as engineering, science, and economics. And the ...