Wasserstein distance, also known as EarthMover's Distance (EMD), is a metric used to measure the difference between two probability distributions. Compared with traditional KL divergence or JS divergence, Wasserstein distance takes into account the structural information between distributions and therefore exhibits better performance in many image processing tasks. By calculating the minimum transportation cost between two distributions, Wasserstein distance is able to measure the minimum amount of work required to transform one distribution into another. This metric is able to capture the geometric differences between distributions, thereby playing an important role in tasks such as image generation and style transfer. Therefore, the Wasserstein distance becomes the concept

Old photo restoration is a method of using artificial intelligence technology to repair, enhance and improve old photos. Using computer vision and machine learning algorithms, the technology can automatically identify and repair damage and flaws in old photos, making them look clearer, more natural and more realistic. The technical principles of old photo restoration mainly include the following aspects: 1. Image denoising and enhancement. When restoring old photos, they need to be denoised and enhanced first. Image processing algorithms and filters, such as mean filtering, Gaussian filtering, bilateral filtering, etc., can be used to solve noise and color spots problems, thereby improving the quality of photos. 2. Image restoration and repair In old photos, there may be some defects and damage, such as scratches, cracks, fading, etc. These problems can be solved by image restoration and repair algorithms

Convolutional neural networks perform well in image denoising tasks. It utilizes the learned filters to filter the noise and thereby restore the original image. This article introduces in detail the image denoising method based on convolutional neural network. 1. Overview of Convolutional Neural Network Convolutional neural network is a deep learning algorithm that uses a combination of multiple convolutional layers, pooling layers and fully connected layers to learn and classify image features. In the convolutional layer, the local features of the image are extracted through convolution operations, thereby capturing the spatial correlation in the image. The pooling layer reduces the amount of calculation by reducing the feature dimension and retains the main features. The fully connected layer is responsible for mapping learned features and labels to implement image classification or other tasks. The design of this network structure makes convolutional neural networks useful in image processing and recognition.

The performance of different PHP functions is crucial to application efficiency. Functions with better performance include echo and print, while functions such as str_replace, array_merge, and file_get_contents have slower performance. For example, the str_replace function is used to replace strings and has moderate performance, while the sprintf function is used to format strings. Performance analysis shows that it only takes 0.05 milliseconds to execute one example, proving that the function performs well. Therefore, using functions wisely can lead to faster and more efficient applications.

PHP provides advanced image processing techniques, including scaling and cropping, image synthesis, filters, conversions, etc. Practical examples show how to use these techniques to create thumbnails that save loading time and showcase images. By understanding these technologies, you can improve your image processing capabilities and enhance application functionality.

Photoshop's advanced photo editing and synthesis technologies include: 1. Use layers, masks and adjustment layers for basic operations; 2. Use image pixel values ??to achieve photo editing effects; 3. Use multiple layers and masks for complex synthesis; 4. Use "liquefaction" tools to adjust facial features; 5. Use "frequency separation" technology to perform delicate photo editing, these technologies can improve image processing level and achieve professional-level effects.

PHP functions have similarities with functions in other languages, but also have some unique features. Syntactically, PHP functions are declared with function, JavaScript is declared with function, and Python is declared with def. In terms of parameters and return values, PHP functions accept parameters and return a value. JavaScript and Python also have similar functions, but the syntax is different. In terms of scope, functions in PHP, JavaScript and Python all have global or local scope. Global functions can be accessed from anywhere, and local functions can only be accessed within their declaration scope.

The main differences between PHP and Flutter functions are declaration, syntax and return type. PHP functions use implicit return type conversion, while Flutter functions explicitly specify return types; PHP functions can specify optional parameters through ?, while Flutter functions use required and [] to specify required and optional parameters; PHP functions use = to pass naming Parameters, while Flutter functions use {} to specify named parameters.