Image Feature Transform Pipeline for Noise Removal and Upscaling
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Solution Overview
Problem
Existing image processing technologies face challenges in effectively removing noise and artifacts and enhancing image resolution, particularly in low-quality images, without adequate methods to leverage repetitive information within the images.
Innovation Solution
An image processing method and apparatus that utilizes a series of transformations and feature data processing steps, including scaling and neural network operations, to enhance image quality and resolution by leveraging repetitive information within the images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing image processing technologies are used to remove noise and artifacts, then image quality improves, but the ability to leverage repetitive information within images is insufficient
Solution Approach 1:
The patent applies copying by extracting repetitive information patterns from multiple regions within the image and using these copied patterns to reconstruct and enhance image quality. The system identifies repeated structures and uses them as templates to fill in missing or degraded areas, effectively leveraging redundancy in the image data to improve overall quality while removing noise and artifacts.
2Measurement precision
If existing image processing technologies are used to enhance image resolution, then image clarity improves, but the method to utilize repetitive information is inadequate
Solution Approach 1:
The patent enhances image resolution by copying and scaling repetitive patterns identified in the image. When high-frequency details are needed for resolution enhancement, the system locates similar repetitive structures elsewhere in the image, copies them, and uses them to reconstruct fine details, thereby improving resolution while effectively utilizing the repetitive information present in the image.
Solution Approach 2:
The patent transforms the problem from a single-image analysis to a multi-dimensional approach by considering repetitive information across different spatial regions and frequency domains. This dimensional expansion allows the system to leverage patterns that repeat across the image in various orientations and scales, providing more information for resolution enhancement than traditional single-region methods.
3Ease of manufacture
If traditional image processing methods are applied, then processing simplicity is maintained, but effectiveness in removing noise and enhancing resolution is limited
Solution Approach 1:
The patent applies preliminary action by first performing a comprehensive analysis of the image to identify and catalog all repetitive information patterns before proceeding with noise removal and resolution enhancement. This preliminary characterization of repetitive structures allows subsequent processing steps to efficiently leverage this pre-identified information, improving effectiveness while maintaining a structured and manageable processing workflow.
Data Source
AI summary
A method of processing an image includes extracting first feature data from a first image, obtaining second feature data by applying, to the first feature data, a first transformation associated with a first parameter, obtaining third feature data by applying, to the second feature data, a second transformation associated with a second parameter, obtaining fourth feature data by performing first image processing on the second feature data and the third feature data, obtaining fifth feature data by applying, to the fourth feature data, a third transformation associated with a third parameter, obtaining sixth feature data by performing second image processing on the second feature data and the fifth feature data, and generating a second image based on the sixth feature data. The first through third parameters are determined based on a comparison of ratios of the parameters with a predetermined value.