AI Image Enhancement Network for Removing Aliasing Artifacts
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Solution Overview
Problem
Existing technologies face challenges in effectively removing aliasing artifacts such as jaggy and broken line artifacts from images, especially when a 3D model for 3D rendering is not available, limiting the ability to collect high-quality and degraded image pairs for training neural networks.
Innovation Solution
The use of artificial intelligence deep learning methods, specifically an image enhancement network, to process degraded images and remove aliasing artifacts by simulating these artifacts on high-quality images to generate paired datasets for training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional image processing methods are used to remove aliasing artifacts, then the process is simple and does not require complex models, but the aliasing artifacts such as jaggy and broken line artifacts cannot be effectively removed
Solution Approach 1:
The patent creates synthetic degraded images by applying aliasing artifacts to high-quality reference images. This copying approach generates training data without requiring actual low-quality captures, enabling the neural network to learn artifact removal patterns while maintaining system simplicity.
Solution Approach 2:
The patent performs preliminary generation of degraded image samples by simulating aliasing artifacts on high-quality images before training begins. This pre-processing step creates the necessary training dataset in advance, eliminating the need for complex real-world data collection during deployment.
2Quantity of substance
If 3D models and rendering systems are used to generate high-quality and degraded image pairs for training, then sufficient training data can be obtained, but access to 3D models is limited to content providers and not available to client devices
Solution Approach 1:
Instead of requiring access to original 3D models and rendering systems, the patent copies the essential degradation patterns by applying synthetic aliasing artifacts to high-quality images. This approach makes training data generation accessible to any device with image processing capabilities, not just content providers with 3D assets.
Solution Approach 2:
The patent introduces an intermediary synthetic degradation process that bridges the gap between high-quality reference images and the appearance of low-quality captured images. This intermediary step creates realistic training pairs without requiring direct access to the original 3D rendering pipeline.
3Manufacturing precision
If deep learning models are trained with synthetic degraded images generated by aliasing artifact simulation, then effective aliasing artifact removal is achieved, but the process requires complex neural network training
Solution Approach 1:
The patent copies realistic degradation patterns through synthetic artifact application, creating training data that accurately represents real-world aliasing without requiring complex data collection pipelines. This simplifies the overall system architecture while maintaining training effectiveness.
Solution Approach 2:
The patent controls the degree and type of synthetic aliasing artifacts applied to training images by adjusting simulation parameters such as sampling rates and artifact intensity. This parameter control enables systematic training data generation with varying degradation levels, improving model robustness without increasing structural complexity.
Data Source
AI summary
A method includes receiving a degraded image including aliasing artifacts and inputting the degraded image to an image enhancement network. This method also includes processing, using the image enhancement network, the degraded image to remove one or more of the aliasing artifacts and outputting, by the image enhancement network, a restored high-quality image. Another method includes obtaining a high-quality image of an environment and generating at least one degraded image of the environment by performing an aliasing artifact simulation on the obtained high-quality image. Performing the aliasing artifact simulation includes (i) performing a broken line artifact simulation to introduce one or more broken line artifacts on one or more objects in the environment of the high-quality image and/or (ii) performing a jaggy artifact simulation to introduce jaggy edges to one or more other objects in the environment of the high-quality image.