20230169626. NEURAL NETWORK SYSTEM AND METHOD FOR RESTORING IMAGES USING TRANSFORMER AND GENERATIVE ADVERSARIAL NETWORK simplified abstract (KWAI INC.)
Contents
- 1 NEURAL NETWORK SYSTEM AND METHOD FOR RESTORING IMAGES USING TRANSFORMER AND GENERATIVE ADVERSARIAL NETWORK
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 NEURAL NETWORK SYSTEM AND METHOD FOR RESTORING IMAGES USING TRANSFORMER AND GENERATIVE ADVERSARIAL NETWORK - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Original Abstract Submitted
NEURAL NETWORK SYSTEM AND METHOD FOR RESTORING IMAGES USING TRANSFORMER AND GENERATIVE ADVERSARIAL NETWORK
Organization Name
Inventor(s)
Ahmed Cheikh Sidiya of Palo Alto CA (US)
NEURAL NETWORK SYSTEM AND METHOD FOR RESTORING IMAGES USING TRANSFORMER AND GENERATIVE ADVERSARIAL NETWORK - A simplified explanation of the abstract
This abstract first appeared for US patent application 20230169626 titled 'NEURAL NETWORK SYSTEM AND METHOD FOR RESTORING IMAGES USING TRANSFORMER AND GENERATIVE ADVERSARIAL NETWORK
Simplified Explanation
The abstract describes a neural network system for restoring images, which includes an encoder and a generative adversarial network (GAN) prior network. The encoder consists of encoder blocks that utilize transformer blocks and convolution layers to generate encoder features and latent vectors from an input image. The GAN prior network consists of pre-trained generative prior layers that take the encoder features and latent vectors as input and produce an output image with super-resolution.
- The neural network system includes an encoder and a GAN prior network.
- The encoder comprises encoder blocks with transformer blocks and convolution layers.
- The encoder receives an input image and generates encoder features and latent vectors.
- The GAN prior network consists of pre-trained generative prior layers.
- The GAN prior network takes the encoder features and latent vectors as input.
- The GAN prior network generates an output image with super-resolution.
Potential Applications
- Image restoration and enhancement
- Super-resolution imaging
- Medical imaging
- Video processing and restoration
Problems Solved
- Restoring and enhancing low-resolution or degraded images
- Improving the quality and resolution of images
- Addressing image restoration challenges in various domains
Benefits
- Advanced neural network system for image restoration
- Utilizes transformer blocks and convolution layers for improved performance
- Incorporates pre-trained generative prior layers for generating high-resolution images
- Enables super-resolution imaging and enhanced visual quality
Original Abstract Submitted
a neural network system for restoring images, a method and a non-transitory computer-readable storage medium thereof are provided. the neural network system includes an encoder and a generative adversarial network (gan) prior network. the encoder includes a plurality of encoder blocks, where each encoder block includes at least one transformer block and one convolution layer, where the encoder receives an input image and generates a plurality of encoder features and a plurality of latent vectors. additionally, the gan prior network includes a plurality of pre-trained generative prior layers, where the gan prior network receives the plurality of encoder features and the plurality of latent vectors from the encoder and generates an output image with super-resolution.
