High-Resolution Image Manipulation via Neural Network Segmentation
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Solution Overview
Problem
Current image manipulation techniques are unable to efficiently handle high-resolution 2D images beyond 512×512 pixels, limiting their application in modern display resolutions such as 1920×1080 or 4K, which require significant resources and labor for realistic results.
Innovation Solution
A neural network-based method that down-samples high-resolution images to generate low-resolution images, computes a motion field, and combines high-frequency residual images to produce high-resolution manipulated images, using a trained manipulation generator with a motion network and refinement network, reducing resource requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional image manipulation methods are used on high-resolution images, then manipulation quality can be maintained, but processing time and computational resources increase significantly
Solution Approach 1:
The patent segments the high-resolution image processing task into two parts: (1) processing a down-sampled low-resolution version of the image to extract manipulation parameters, and (2) applying these parameters to the original high-resolution image. This segmentation allows the computationally intensive manipulation algorithm to operate on smaller data while preserving high-resolution output quality.
Solution Approach 2:
The patent introduces a down-sampled low-resolution image as an intermediary representation. This intermediary contains the essential structural information needed for manipulation parameter extraction, serving as a bridge between the original high-resolution image and the manipulation algorithm, thereby reducing computational burden while maintaining manipulation accuracy.
2Adaptability or versatility
If existing 2D image manipulation models are applied to high-resolution images, then manipulation capability is provided, but memory resources and processing power requirements become prohibitive
Solution Approach 1:
The patent divides the image data into low-resolution and high-resolution components, processing only the low-resolution version through the manipulation generator. This segmentation enables the manipulation capability to be achieved with reduced processing power, as the neural network operates on downscaled data while the final output maintains high resolution.
Solution Approach 2:
The patent creates a down-sampled copy of the original high-resolution image for processing. This copy contains sufficient information for extracting manipulation parameters but requires significantly less memory and processing power. The manipulation results are then transferred back to the original high-resolution image, achieving versatility with reduced resource consumption.
3Manufacturing precision
If high-resolution images are processed directly by neural networks, then output quality is maintained, but device compatibility is limited due to resource constraints
Solution Approach 1:
The patent segments the processing workload by extracting manipulation parameters from down-sampled images rather than processing full high-resolution images through the neural network. This segmentation enables the system to run on devices with limited resources while still producing high-resolution output, thereby improving device compatibility without sacrificing output quality.
Solution Approach 2:
The patent changes the resolution parameter of the input image for neural network processing, using down-sampled versions instead of full-resolution images. This parameter change reduces computational requirements and enables deployment on mobile devices and other resource-constrained platforms while maintaining the ability to generate high-quality manipulated images.
Data Source
AI summary
Methods and systems for high-resolution image manipulation are disclosed. An original high-resolution image to be manipulated is obtained, as well as a driving signal indicating a manipulation result. The original high-resolution image is down-sampled to obtain a low-resolution image to be manipulated. Using a trained manipulation generator, a low-resolution manipulated image and a motion field are generated from the low-resolution image. The motion field represent pixel displacements of the low-resolution image to obtain the manipulation indicated by the driving signal. A high-frequency residual image is computed from the original high-resolution image. A high-frequency manipulated residual image is generated using the motion field. A high-resolution manipulated image is outputted by combining the high-frequency manipulated residual image and a low-frequency manipulated image generated from the low-resolution manipulated image by up-sampling.