I recently read some papers and blogs on image generation, and I think I should summarize them. This paper mainly introduces image generation technology, including research background, research significance, related applications, and the technologies used.
Table of contents
1. Background and Significance
2. Image generation application
2.5 Interactive image generation
3.1 Generating Adversarial Networks
1. Background and Significance
Usually, deep learning (supervised learning) needs to rely on a large amount of data to train the network. The more data, the better the generalization performance of the trained network . However, collecting data requires human and financial resources , which makes researchers only collect enough data for relatively important tasks. Therefore, the training data for most vision tasks is still missing or insufficient. This makes the deep learning network for this task not have enough generalization ability, and cannot be applied well.
Most vision tasks share the same characteristic: difficult data acquisition , which prevents researchers from using deep learning algorithms to solve tasks. Therefore, how to collect data or increase the amount of data has become the primary task to solve most vision problems. For the problem of no training data or too little data , researchers have proposed unsupervised training, image generation (data augmentation) and other methods.
Figure 1.1 Example of image generation
The image generation task aims to automatically generate corresponding images based on user needs. (significance)
- On the one hand , the research on image generation can quickly construct massive data for deep learning training , generate data required for scene tasks, and further promote the computer's understanding of image information.
- On the other hand , the development of image generation has also brought interesting and important applications such as image editing, image restoration, and text-to-image conversion , as shown in Figure 1.2. It can be seen that image generation, as an important research direction in the field of computer vision, has great application potential.
Figure 1.2 Some applications of image generation
According to the classification of generated image content, image generation tasks can be divided into two types: single object image generation and multi-object scene image generation. Single-object image generation only needs to pay attention to the generation details of a single object . The generation of scene images often needs to consider multiple instance objects . Objects need to satisfy reasonable and appropriate semantic layout relationships that are suitable for user needs, so the complexity of the scene image generation task is high. , is challenging and has rich theoretical research significance.
Through the scene image generation technology, the computer can create a virtual artwork of the scene specified by the user, and can also automatically realize the graphic design and furniture layout of multiple objects according to the user's needs. Therefore, the scene image generation also has a wide range of practical application values.
2. Image generation application
Image generation applications are widely used in many fields to generate realistic and innovative image content through algorithms and models. These applications include image synthesis and inpainting, image enhancement and super-resolution, image style transfer, image generation and filling, video generation and dynamic scene synthesis, etc.
In image generation and filling applications, new image samples are generated by technologies such as generative confrontation networks, data sets are expanded, data enhancement is used, and the generalization ability and performance of deep learning models are improved. At the same time, complete images can also be generated based on a given partial image , to fill in the missing regions.
2.1 Image-to-Image Conversion
Image-to-image conversion is to transform an input image into an output image with a specific target attribute or style . This technology realizes image conversion and modification by training a model to learn the mapping relationship between an input image and a target image.
An example of image-to-image conversion, as shown in Figure 2.1, generates a real scene map from a simple sketch with a driving scene, generates a road topology map from a satellite image with a map scene, and generates a color map with a grayscale image, etc.
Figure 2.1 Example of image-to-image conversion
Image-to-image conversion based on deep learning has become the mainstream method. Here are some common deep learning-based techniques:
a. Generative Adversarial Network (GAN) : GAN is a neural network model for generating new images. It consists of a generator and a discriminator . The generator tries to generate realistic output images , while the discriminator tries to distinguish generated images from real ones . Through adversarial training, the generator gradually learns how to generate outputs similar to real images.
b. Autoencoder : An autoencoder is a neural network model for learning low-dimensional representations of data. In image-to-image translation, autoencoders can be used for feature extraction and reconstruction. By encoding the input image into a low-dimensional representation and then decoding it into an output image , the autoencoder can learn the feature representation and reconstruction ability of the input image .
c. Conditional generation model : The conditional generation model introduces additional conditional information during the generation process. In image-to-image translation, conditions can be feature, style, or semantic information of the input image . More precise and controllable image translation can be achieved by jointly training the conditional information with the input of the generative model.
These image-to-image conversion tasks use various machine learning and deep learning techniques, including convolutional neural network (CNN), generative adversarial network (GAN), image optimization algorithm, etc. The development of these technologies makes image conversion and modification more precise, efficient and flexible, and provides powerful tools and methods for image processing and applications.
2.2 Text-to-image generation
It can generate images from text descriptions with steps similar to image-to-image translation. An example of text-to-image generation, as shown in Figure 2.2, inputs a paragraph of text, and the model analyzes the meaning of the text to generate a corresponding image.
Figure 2.2 Example of text-to-image generation
A text-to-image generation process typically consists of the following steps:
1. Text representation : First, the input text description is converted into a computer-understandable vector representation . This can be achieved by mapping words to word vectors (e.g. Word2Vec, GloVe) or using pre-trained language models (e.g. BERT, GPT). This captures semantic and contextual information in the text.
2. Image generation model : The key to generating images based on text descriptions is to design an effective image generation model. Commonly used models include convolutional neural network CNN, recurrent neural network RNN and generative confrontation network GAN. These models learn the mapping between text and images to generate images that match text descriptions .
3. Training process : During training, the model receives textual descriptions as input and tries to generate images that match the descriptions . The model continuously optimizes its parameters by minimizing the difference between the generated image and the real image. This usually involves using a loss function (such as pixel-level loss or perceptual loss) to measure the quality of the resulting image.
4. Evaluate and improve : The generated images need to undergo a process of evaluation and improvement. Evaluation can use human evaluations, such as subjective scoring or comparison experiments, and objective evaluation metrics, such as image quality evaluation metrics. Based on the evaluation results, the generative model can be tuned and improved to improve the quality and accuracy of the generated images.
At present, a representative summary of deep learning-based text-to-image generation methods is shown in Figure 2.3.
Figure 2.3 Summary of text-to-image generation methods
Text-to-image generation techniques have potential applications in many application domains. For example, it can be used to assist in the construction of virtual scenes, help people transform ideas and descriptions into visual forms, and assist in the creation of works of art . As the fields of deep learning and natural language processing continue to develop, so will text-to-image generation techniques.
2.3 Image super-resolution
Image super-resolution refers to the process of converting low-resolution images into high-resolution images through algorithms and techniques . In short, it can make the details of the image more clear and precise . The goal of image super-resolution is to increase the spatial resolution of an image, that is, to increase the amount and clarity of visible details in an image. This is very important for many applications such as image magnification, vision enhancement, medical image analysis , etc.
Super-resolution is the process of creating a high-resolution image from a smaller image, such as an image with a resolution of 640x480, and through this technique, an ultra-high-definition image of 2560x1920. An example of the effect is shown in Figure 2.4.
Figure 2.4 Example of image super-resolution
There are several approaches to image super-resolution, the following are some of the common ones:
A. Interpolation method :
Interpolation is a simple and straightforward method for image super-resolution. It increases the resolution of an image by filling new pixels between known pixels. The simplest interpolation method is linear interpolation, which uses a weighted average of neighboring pixels to generate a new pixel value . More advanced interpolation methods, such as bilinear interpolation, bicubic interpolation, etc., can provide better image detail preservation and smoothing effects.
B. Edge-based approach :
Edge-based methods utilize edge information in images to increase resolution . Edges are places in an image where there is a change in color or brightness, usually indicating the boundaries or details of an object. By identifying and enhancing edges, more details can be recovered from low-resolution images and high-resolution images can be generated.
C. Deep learning-based methods :
In recent years, deep learning has achieved important breakthroughs in image super-resolution. By using deep neural networks, such as convolutional neural network (CNN) and generative adversarial network (GAN) , the mapping from low-resolution images to high-resolution images can be learned . These models learn the features and structures of images through a large number of training samples and generate high-quality super-resolution images.
- && Typical convolutional neural network super-resolution models include SRCNN and ESPCN. These models gradually increase the resolution of images through multiple convolutional layers and upsampling operations, and utilize large amounts of training data to learn image details and structures.
- && Generative confrontation network methods have made important breakthroughs in image super-resolution, among which ESRGAN is a widely used model. It enhances the quality and details of super-resolution images by stacking multiple layers of generators and discriminators.
The key to image super-resolution methods based on deep learning lies in large-scale training data and suitable network architecture . Typically, high-resolution images and corresponding low-resolution image pairs are used for training, enabling the network to learn feature and texture information in the images. In addition, in order to improve the quality of super-resolution images, loss functions, such as mean square error and perceptual loss functions, can also be used to guide the training process of the network.
Image super-resolution technology has a wide range of applications in many fields. For example, in digital photography, it can help improve image quality and magnify image details; in video processing, it can improve video clarity and detail presentation; in medical imaging, it can help doctors diagnose and analyze images more accurately .
In general, image super-resolution is an important technology, which can convert low-resolution images into high-resolution images through different methods and algorithms, and improve the clarity and details of images.
2.4 Style transfer
Style transfer can combine the style of one image with the content of another image to generate an image with a novel artistic style . This technology combines machine learning and deep learning methods to allow computers to understand and apply styles and content between different images.
Style transfer is the process of transferring different styles to any image. The example effect is shown in Figure 2.5.
Figure 2.5 Example of style transfer
Style transfer methods mainly include optimization-based methods and neural network-based methods. An optimization-based style transfer method using convolutional neural networks (CNN) and image optimization algorithms. These methods optimize the pixel values of the generated image by minimizing a loss function such that it matches the feature statistics of both the content image and the style image.
The neural network-based style transfer method uses deep learning models such as generative confrontation network GAN and convolutional neural network CNN to learn the mapping relationship of style transfer by training the network.
Style transfer technology has a wide range of applications in the fields of art creation, image editing and design. It allows us to apply different styles of artistic features to our own images to create personalized works of art. In addition, style transfer can also be used in image enhancement, image style transfer, and virtual reality, which brings new possibilities for image processing.
2.5 Interactive image generation
Interactive image generation, which allows users to generate images through real-time, two-way interaction with a computer system. Compared with traditional image generation methods, interactive image generation gives users more control and participation, allowing them to directly participate in the image creation process.
Figure 2.6 Example of interactive image generation
As shown in Figure 2.6, images are generated from edited shapes and colors. The green strokes at the bottom create the grasslands, and the rectangles create the skyscrapers. Images will be generated and fine-tuned with further input from the user. The generated images can also be used to retrieve the most similar real image available. Providing interactive image generation is a whole new way to intuitively search for images.
Interactive image generation methods usually combine techniques from the fields of computer vision, computer graphics, and human-computer interaction. Deep learning generative models are common interactive image generation methods, such as generative confrontation network GAN and variational autoencoder VAE, which can be used for interactive image generation . These models can generate images based on user input and feedback through real-time interaction with the user. Users can guide the generation process by providing different inputs to the model, such as textual descriptions, sketches, or guiding images.
Interactive image generation can not only be used for personal artistic creation and entertainment, but also play an important role in the fields of design, virtual reality, and human-computer interface . It provides a more direct, flexible and creative way to allow users to participate in the process of image generation, enabling personalized and customized image creation.
3. Image Synthesis Technology
Image synthesis technology can be divided into traditional algorithms and deep learning algorithms , where traditional algorithms include rule-based synthesis methods, image fusion, texture-based synthesis, etc.
Rule-based synthesis methods : Rule-based synthesis methods use predefined rules and algorithms to generate synthetic imagesThese rules can include geometric transformations (such as translation, rotation, scaling), color adjustments, texture repetition, etc. For example, in image editing software, images can be composited using layer blending modes, masks, filters, and more. Rule-based synthesis methods are suitable for simple synthesis tasks and can be synthesized without requiring large amounts of data and complex models.
Image fusion : Image fusion is to fuse some parts or features of two or more images to generate a composite image with the characteristics of both. Common image fusion methods include pixel-level fusion, multiple exposure, average fusion, Laplacian pyramid fusion,etc. Pixel-level fusion fuses two or more images together through pixel-level operations, such as pixel-by-pixel averaging and pixel-by-pixel weighted fusion. Multiple exposure is to superimpose the brightness information of multiple images to obtain a comprehensive effect. Laplacian pyramid fusion uses pyramid structure and Laplacian pyramid image representation, and fuses different levels and frequency information of synthetic images.
Texture-based synthesis : Texture-based synthesis methods use existing texture samples to generate new texture images . These methods can. Among them, the method based on Markov Random Field (MRF) uses a probabilistic model to model the statistical distribution of texture features, and generates new texture images through sampling and optimization algorithms. Another method is to use texture synthesis algorithms to generate synthetic images with similar texture features by analyzing the local features and structural information of texture samples.
Image synthesis algorithms based on deep learning mainly include generative confrontation network GAN, variational autoencoder VAE, etc. The current mainstream image synthesis algorithm is based on generative confrontation network.
Generative Adversarial Network , which consists of a generator and a discriminator. The generator is responsible for generating realistic images, and the discriminator is used to evaluate the realism of the generated images. By adversarial training of the generator and the discriminator, the generative adversarial network can learn the distribution of the data and generate realistic synthetic images.
Variational autoencoders , a generative model based on probabilistic graphical models, are used to learn latent representations of input data and generate new samples. It learns latent variable representations of the data and uses these variables to generate new images.
3.1 Generating Adversarial Networks
Generative Adversarial Networks (GANs) are deep learning models used to generate realistic synthetic data such as images, audio, or text.
GAN consists of two main components: Generator and Discriminator . The generator is responsible for generating realistic synthetic data , while the discriminator is responsible for evaluating whether the input data
Figure 3.1 GAN model structure
During training, the generator and the discriminator are trained against each other. The generator receives a random noise vector as input and undergoes a series of transformation and mapping operations to generate synthetic data similar to real data . The discriminator takes real data and data generated by the generator and distinguishes them . The goal of the generator is to deceive the discriminator as much as possible so that it cannot distinguish real data from generated data, while the goal of the discriminator is to identify real data from generated data as accurately as possible.
Figure 3.2 GAN structure
As shown in Figure 3.2, the training process of GAN is shown. In a, the discriminant distribution (dashed blue line) is updated so that it can distinguish whether the input comes from the real distribution (dashed black line) or the generated data distribution (solid green line). In b, the discriminator is trained to distinguish between real and fake data, and it is easy to do the task. In Figure c, the discriminator is fixed and only the generator is trained so that the distribution of the generated fake data is closer to the real data distribution. The update continues until the discriminator cannot tell the difference (d) By repeatedly iteratively training the generator and the discriminator, GAN can gradually improve the quality and fidelity of the synthetic data generated by the generator, making it close to the distribution of real data. The dynamic game in the training process prompts the generator and the discriminator to compete and improve each other, and finally reach a dynamic equilibrium point.
In the field of image generation, there are many GAN-based algorithms. Here are some common algorithms:
- Original GAN (GAN) : The earliest proposed generative adversarial network, consisting of a generator and a discriminator. The generator is responsible for generating realistic image samples, and the discriminator is responsible for distinguishing the generated images from real ones. Through adversarial training, the generator and the discriminator compete and optimize against each other, so that the generator generates more realistic images and the discriminator more accurately distinguishes real and synthetic images.
- Conditional GAN (cGAN) : cGAN introduces conditional information based on the original GAN. By feeding the generator and discriminator with conditional vectors,such as labels or textual descriptions, images with specific properties can be generated. cGAN is widely used in image-to-image translation tasks, such as image style transfer and semantic segmentation to image translation.
- Deep Convolutional GAN (DCGAN ): It is a variant of GAN based on Convolutional Neural Networks, specifically for image generation. It uses convolutional and deconvolutional layers in the generator and discriminator, and uses activation functions such as batch normalization and LeakyReLU to stabilize training and generate high-quality images.
- Wasserstein GAN (WGAN) : It improves the training stability of GAN by using the Wasserstein distance (Earth-Mover distance) as the loss function. WGAN focuses on the distribution difference between the image generated by the generator and the real image, so that the image generated by the generator is closer to the real distribution . In addition, WGAN also uses tricks such as weight clipping to alleviate the mode collapse problem during training.
- Progressive GAN (PGAN) : It is a progressively trained GAN method for generating images of gradually increasing resolution . It starts training the generator and discriminator at a low resolution and gradually increases the resolution while taking the previously trained model as initial parameters. With progressive training, PGAN is able to generate higher quality and more detailed images.
In addition to the above algorithms, there are many other GAN variants and improved methods, such as CycleGAN, StarGAN, StyleGAN, etc. These algorithms play an important role in the field of image generation, constantly promoting the improvement of the quality and diversity of image generation.
Take a look at the table: GAN- derived variant classification
3.2 Variational Autoencoders
Variational Autoencoder (VAE) is a generative model based on probabilistic graphical models, which is used to learn the latent representation of the input data and generate new samples .
The basic idea of VAE is to encode the input data into latent variables in the latent space, and then decode from the latent space to generate new samples. Different from traditional autoencoders, VAE introduces the concept of probability distribution. By modeling latent variables, VAE can learn and sample data distribution.
The structure of VAE includes encoder (Encoder) and decoder (Decoder) , as shown in Figure 3.3. An encoder maps input data to latent variables in a latent space, typically expressed as parameters of mean and variance . These parameters are used to define the probability distribution of the latent variable, usually assumed to be Gaussian.
Figure 3.3 GAN structure
During training , VAE learns the parameters of the model by maximizing the marginal likelihood of the observed data . To achieve this, VAE introduces the reparameterization trick to obtain the actual latent variable values by sampling from the probability distribution of the latent variables . In this way, by decoding the latent variables through the decoder, new samples similar to the original input data can be generated. VAEs can be used not only to generate new samples, but also for data compression and visualization of latent spaces. Control and editing of the generated samples can be achieved by interpolating and manipulating the latent variables in the latent space.
To summarize, a variational autoencoder is a probabilistic graphical model used to learn latent representations of data and generate new samples. It maps input data to latent variables in the latent space through an encoder, and generates new samples from latent variables through a decoder. The model is trained by maximizing the marginal likelihood of the observed data, sampled by a reparameterization trick. Variational autoencoders have a wide range of applications in tasks such as generative models and data compression.
references
[1] Sun Shukui , Fan Jing , Qu Jinshuai, Lu Peidong . A Review of Generative Adversarial Network Research [J]. Computer Engineering and Applications. 2022
[2] Wang Yuhao , He Yu , Wang Zhu . A review of text-to-image generation methods based on deep learning [J]. Computer Engineering and Applications. 2022
[3] Xiao Hewen . Scene image generation guided by knowledge graph [D]. Dalian University of Technology . 2022
[4] Zong Yujia . Two-stage sketch-to-image generation model and application implementation [D]. Dalian University of Technology . 2021
[5] Wen Qiang . Research on image generation technology based on recurrent generative confrontation network [D]. South China University of Technology. 2020
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