Image generation method and system based on AI deep learning
By generating adversarial networks, variational autoencoders and diffusion models, the problem of insufficient image quality and diversity in the prior art is solved, and efficient and low-cost image generation is achieved, which is suitable for artistic creation, entertainment industry and product design.
Patent Information
- Application Number
- CN202510422767.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-07
- Publication Date
- 2025-07-11
AI Technical Summary
Existing deep learning-based image generation methods have shortcomings in image quality and diversity, and data set collection and preprocessing are difficult, requiring expertise and high costs.
Generative adversarial networks, variational autoencoders and diffusion models are adopted, combined with data preprocessing and model training optimization, to generate high-quality and high-resolution images, and provide a friendly user interaction interface.
Generating high-quality and diverse images reduces human and material costs, improves creative efficiency and image generation speed, and is suitable for artistic creation, entertainment industry and product design.
Smart Images
Figure CN120298524A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of artificial intelligence technology, and specifically to an image generation method and system based on AI deep learning. Background Art
[0002] With the rapid development of artificial intelligence technology, deep learning, as one of its core branches, has achieved remarkable results in the field of image generation. The image generation method based on deep learning can generate realistic new images by learning the features of a large amount of image data, and is widely used in multiple fields such as art creation, medical image analysis, virtual reality, and game development. Although deep learning models have achieved remarkable results in image generation, they still face technical stability problems and need to be continuously optimized and improved. Data quality and diversity: High-quality data sets are the basis for successful training. However, in practical applications, collecting a large number of relevant images and preprocessing them is a challenging task. Model selection and training: Selecting appropriate deep learning models and training strategies is crucial for generating high-quality images, which requires rich experience and professional knowledge. Therefore, the present application now proposes an image generation method and system based on AI deep learning. Summary of the Invention
[0003] (I) Technical Problems to be Solved Aiming at the deficiencies of the prior art, the present invention provides an image generation method and system based on AI deep learning, which has the advantages of diversity and creativity stimulation, and reducing costs to achieve resource optimization, and solves the deficiencies of the prior art.
[0004] (II) Technical Solutions To achieve the above object, the present invention provides the following technical solutions: An image generation method and system based on AI deep learning, including a generative adversarial network, a variational autoencoder, a diffusion model, an image generation system and an application scenario. The specific steps are as follows: S1. Collect and preprocess image data, and clean, normalize, and enhance the data; S2. Select a deep learning model and use the preprocessed image data for training; S3. Continuously adjust the parameters of the model during training to optimize the quality of the generated images; S4. After the model training is completed, use the trained model to generate new images; S5. Post-process the generated images to remove noise, enhance details, and adjust colors.
[0005] Preferably, the generative adversarial network consists of two networks, a generator and a discriminator. The generator is responsible for generating images, and the discriminator is responsible for evaluating whether the images look like real images. The generator and the discriminator compete with each other during the training process. The generator tries to create more and more realistic images, and the discriminator distinguishes between real images and generated images. The generative adversarial network can generate high-resolution and realistic images, such as natural landscapes and face images. In addition, it can also be used for tasks such as image restoration, image denoising, image style transfer, conditional image generation, and image super-resolution.
[0006] Preferably, the variational autoencoder is a generative model based on probabilistic graphical models, consisting of two parts, an encoder and a decoder. The encoder is responsible for mapping the input data into a latent space, and the decoder reconstructs the original input from this latent space. The variational autoencoder can perform unsupervised learning without labels, but its generation quality may be inferior to some more advanced models when dealing with high-resolution images. The variational autoencoder is usually used to generate new images and model the latent attributes of the input data.
[0007] Preferably, the diffusion model is a relatively new class of generative models. They gradually convert random noise into real images through a series of small noise addition steps. This process can be regarded as a "reverse" thermodynamic process. The key of the diffusion model lies in designing effective denoising steps to ensure that the finally generated images are realistic. The diffusion model can achieve excellent results on multiple benchmark tests, and the generated images have high quality and diversity. The diffusion model is widely used in tasks such as image denoising, image enhancement, and image segmentation.
[0008] Preferably, the application scenarios include art creation, the entertainment industry, product design, and medical image processing, where: Art creation: Provide artists with new creative methods and sources of inspiration, and generate artistic works with creativity and uniqueness.
[0009] Entertainment industry: In film and game development, generate realistic special effects and scenes to provide users with a more real and immersive experience.
[0010] Product design: In the field of product design, generate product models for various solutions, quickly conduct product design and verification, improve design efficiency and quality, and reduce costs.
[0011] Preferably, the image generation system includes a data processing module, a model training module, an image generation module, and a user interaction module. The specific working steps are as follows: Step 1: The data processing module is responsible for collecting, cleaning, and preprocessing image data to provide high-quality input for the deep learning model. Step 2: The model training module selects a deep learning algorithm and uses a large-scale dataset for model training; Step 3: Use the trained deep learning model to generate images according to user input; Step 4: Provide a friendly user interface and interaction method, enabling users to conveniently input instructions, select parameters, and view the generated images; Step 5: Provide real-time feedback and modification to improve the flexibility and convenience of user creation.
[0012] Compared with the prior art, the present invention provides an image generation method and system based on AI deep learning, having the following beneficial effects: 1. For the image generation method and system based on AI deep learning, the AI deep learning model can learn the features and rules of images through training, so as to quickly generate high-quality images. Compared with traditional manual drawing or photography, the speed of AI-generated images is significantly improved, and a large number of images can be generated in a short time. In the creative industry, AI image generation technology can greatly shorten the creation cycle and improve work efficiency. Artists and designers can use AI-generated images as a source of inspiration or creative materials to accelerate the creative process.
[0013] 2. For the image generation method and system based on AI deep learning, the AI deep learning model can generate images with different styles, types, and themes to meet the diverse needs of users. The AI deep learning model can generate images with high fidelity, high resolution, and high realism.
[0014] 3. For the image generation method and system based on AI deep learning, compared with traditional manual drawing or photography, AI-generated images do not require too much human and material costs. Users only need to input some keywords or descriptions, and AI can quickly generate the required images, thus significantly reducing production costs. By generating a large number of image resources, the production processes of games and movies can be optimized, and work efficiency can be improved. BRIEF DESCRIPTION OF THE DRAWINGS
[0015] Figure 1 It is a schematic diagram of the image generation method of deep learning of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0016] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.
[0017] An image generation method and system based on AI deep learning, including a generative adversarial network, a variational autoencoder, a diffusion model, an image generation system, and application scenarios. The specific steps are as follows: S1. Collect and preprocess image data, and perform cleaning, normalization, and enhancement on the data; S2. Select a deep learning model and use the preprocessed image data for training; S3. Continuously adjust the parameters of the model during the training process to optimize the quality of the generated images; S4. After the model training is completed, use the trained model to generate new images; S5. Post-process the generated images, remove noise from the images, enhance details, and adjust colors.
[0018] Furthermore, the generative adversarial network consists of two networks, a generator and a discriminator. The generator is responsible for generating images, and the discriminator is responsible for evaluating whether the images look like real images. The generator and the discriminator compete with each other during the training process. The generator tries to create increasingly realistic images, and the discriminator distinguishes between real images and generated images. The generative adversarial network can generate high-resolution and realistic images, such as natural landscapes and face images. In addition, it can also be used for tasks such as image restoration, image denoising, image style transfer, conditional image generation, and image super-resolution.
[0019] Furthermore, the variational autoencoder is a generative model based on a probabilistic graphical model, consisting of an encoder and a decoder. The encoder is responsible for mapping the input data into a latent space, and the decoder reconstructs the original input from this latent space. The variational autoencoder can perform unsupervised learning without labels, but its generation quality may be inferior to some more advanced models when dealing with high-resolution images. The variational autoencoder is usually used to generate new images and model the latent attributes of the input data.
[0020] Furthermore, the diffusion model is a relatively new type of generative model. They gradually transform random noise into real images through a series of small noise addition steps. This process can be regarded as a "reverse" thermodynamic process. The key of the diffusion model lies in designing effective denoising steps to ensure that the finally generated images are realistic. The diffusion model can achieve excellent results on multiple benchmark tests, and the generated images have high quality and diversity. The diffusion model is widely used in tasks such as image denoising, image enhancement, and image segmentation.
[0021] Furthermore, the application scenarios include art creation, the entertainment industry, product design, and medical image processing, where: Art creation: Provide artists with new creative ways and sources of inspiration, and generate artistic works with creativity and uniqueness.
[0022] Entertainment industry: In movie and game development, generate realistic special effects and scenes to provide users with a more real and immersive experience.
[0023] Product design: In the field of product design, generate product models for various solutions, quickly conduct product design and verification, improve design efficiency and quality, and reduce costs.
[0024] Furthermore, the image generation system includes a data processing module, a model training module, an image generation module, and a user interaction module. The specific working steps are as follows: Step 1: The data processing module is responsible for collecting, cleaning, and preprocessing image data to provide high-quality input for the deep learning model. Step 2: The model training module selects a deep learning algorithm and uses a large-scale dataset for model training. Step 3: Use the trained deep learning model to generate images according to user input. Step 4: Provide a friendly user interface and interaction method, enabling users to conveniently input instructions, select parameters, and view the generated images. Step 5: Provide real-time feedback and modification to improve the flexibility and convenience of user creation.
[0025] Example 1: An image generation method and system based on AI deep learning, including a generative adversarial network, a variational autoencoder, a diffusion model, an image generation system, and application scenarios. The specific steps are as follows: S1: Collect and preprocess image data, clean, normalize, and enhance the data. S2: Select a deep learning model and use the preprocessed image data for training. S3: Continuously adjust the parameters of the model during training to optimize the quality of the generated images. S4: After the model training is completed, use the trained model to generate new images. S5: Post-process the generated images, remove noise, enhance details, and adjust colors.
[0026] Example 2: An image generation method and system based on AI deep learning proposed according to Embodiment 1. The generative adversarial network consists of two networks, a generator and a discriminator. The generator is responsible for generating images, and the discriminator is responsible for evaluating whether the images look like real images. The generator and the discriminator compete with each other during the training process. The generator tries to create more and more realistic images, and the discriminator distinguishes between real images and generated images. The generative adversarial network can generate high-resolution and realistic images, such as natural landscapes and face images. In addition, it can also be used for tasks such as image restoration, image denoising, image style transfer, conditional image generation, and image super-resolution. The AI deep learning model can learn the features and patterns of images through training, thus quickly generating high-quality images. Compared with traditional manual drawing or photography, the speed of AI-generated images is significantly improved, and a large number of images can be generated in a short time. In the creative industry, AI image generation technology can greatly shorten the creation cycle and improve work efficiency. Artists and designers can use AI-generated images as a source of inspiration or creative materials to accelerate the creative process.
[0027] Embodiment 3: An image generation method and system based on AI deep learning proposed according to Embodiment 1. The variational autoencoder is a generative model based on probabilistic graphical models, consisting of an encoder and a decoder. The encoder is responsible for mapping the input data into a latent space, and the decoder reconstructs the original input from this latent space. The variational autoencoder can perform unsupervised learning without labels, but its generation quality may be inferior to some more advanced models when dealing with high-resolution images. The variational autoencoder is usually used to generate new images and model the latent attributes of the input data. Diffusion models are a relatively new class of generative models. They gradually transform random noise into real images through a series of small noise addition steps. This process can be regarded as a "reverse" thermodynamic process. The key to diffusion models lies in designing effective denoising steps to ensure that the finally generated images are realistic. Diffusion models can achieve excellent results on multiple benchmark tests, and the generated images have high quality and diversity. Diffusion models are widely used in tasks such as image denoising, image enhancement, and image segmentation. The AI deep learning model can generate images with different styles, types, and themes to meet the diverse needs of users. The AI deep learning model can generate images with high fidelity, high resolution, and high realism.
[0028] Embodiment 4: An image generation method and system based on AI deep learning proposed according to Embodiment 1. The application scenarios include art creation, the entertainment industry, product design, and medical image processing, where: Artistic creation: Provide artists with new ways of creation and sources of inspiration, and generate artistic works with creativity and uniqueness.
[0029] Entertainment industry: In film and game development, generate realistic special effects and scenes to provide users with a more real and immersive experience.
[0030] Product design: In the field of product design, generate product models for various solutions, quickly conduct product design and verification, improve design efficiency and quality, and reduce costs; Compared with traditional manual drawing or photography, AI-generated images do not require much human and material cost. Users only need to input some keywords or descriptions, and the AI can quickly generate the required images, thus significantly reducing production costs. By generating a large number of image resources, the production processes of games and movies can be optimized, and work efficiency can be improved.
[0031] Example Five: An image generation method and system based on AI deep learning proposed according to Example One. The image generation system includes a data processing module, a model training module, an image generation module, and a user interaction module. The data processing module is responsible for collecting, cleaning, and preprocessing image data to provide high-quality input for the deep learning model. The model training module selects a deep learning algorithm and uses a large-scale data set for model training. Using the trained deep learning model, generate images according to user input, provide a friendly user interface and interaction method, enable users to conveniently input instructions, select parameters, and view the generated images, and provide real-time feedback and modification to improve the flexibility and convenience of user creation.
[0032] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.
Claims
1. An image generation method and system based on AI deep learning, characterized in that: Including generative adversarial networks, variational autoencoders, diffusion models, image generation systems and application scenarios, the specific steps are as follows: S1. Collect and preprocess image data, and clean, normalize, and enhance the data; S2. Select a deep learning model and use the preprocessed image data for training; S3. Continuously adjust the parameters of the model during training to optimize the quality of the generated images; S4. After the model training is completed, use the trained model to generate new images; S5. Post-process the generated images to remove noise, enhance details, and adjust colors.
2. The image generation method and system based on AI deep learning according to claim 1, wherein: The generative adversarial network consists of two networks, a generator and a discriminator. The generator is responsible for generating images, and the discriminator is responsible for evaluating whether the images look like real images. The generator and the discriminator compete with each other during training.
3. A method and system for image generation based on AI deep learning according to claim 1, characterized in that: The variational autoencoder is a generative model based on probabilistic graphical models, consisting of an encoder and a decoder. The encoder is responsible for mapping the input data into a latent space, and the decoder reconstructs the original input from this latent space.
4. A method and system for image generation based on AI deep learning according to claim 1, characterized in that: The diffusion model is a type of generative model. The key to the diffusion model lies in designing effective denoising steps so that the finally generated images are realistic. Diffusion models are widely used in image denoising, image enhancement, and image segmentation tasks.
5. A method and system for image generation based on AI deep learning according to claim 1, characterized in that: The application scenarios include art creation, the entertainment industry, product design, and medical image processing.
6. A method and system for image generation based on AI deep learning according to claim 1, characterized in that: The image generation system includes a data processing module, a model training module, an image generation module, and a user interaction module. The specific working steps are as follows: Step 1. The data processing module is responsible for collecting, cleaning, and preprocessing image data to provide high-quality input for the deep learning model; Step 2. The model training module selects a deep learning algorithm and uses a large-scale dataset for model training; Step 3. Use the trained deep learning model to generate images according to user input; Step 4. Provide a friendly user interface and interaction method to enable users to conveniently input instructions, select parameters, and view the generated images; Step 5. Provide real-time feedback and modification to improve the flexibility and convenience of user creation.