Super-Resolution Image Error Mapping with Rolling Fourier Correlation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image reconstruction techniques introduce errors and artifacts, leading to low image quality and misinterpretation of information, particularly in super-resolution imaging, which current quality assessments are subjective and computationally expensive.
Innovation Solution
The use of a rolling Fourier ring correlation (rFRC) computation and pixel-level analysis of error locations (PANEL) framework to generate a target map that quantitatively maps errors in images without relying on ground-truth references, merging with a resolution scaled error map (RSM) for comprehensive error detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reconstruction techniques are used to generate images, then image acquisition capability is improved, but image errors and artifacts are introduced
Solution Approach 1:
The patent introduces an intermediary error map that mediates between the reconstructed image and ground truth. This error map quantifies reconstruction errors without requiring direct access to ground truth, allowing objective quality assessment while maintaining the benefits of reconstruction techniques.
Solution Approach 2:
The patent implements a feedback mechanism where the error map is used to evaluate reconstruction quality and guide optimization. By continuously measuring errors through the error map and using this information to adjust reconstruction parameters, the system improves image quality while maintaining reconstruction capabilities.
2Ease of operation
If subjective quality assessment methods are used, then implementation simplicity is improved, but measurement precision deteriorates
Solution Approach 1:
The patent replaces subjective human visual assessment with an objective computational error map generation system. This substitution uses automated image processing algorithms to calculate quantitative error metrics, eliminating subjectivity while maintaining ease of implementation through algorithmic automation.
Solution Approach 2:
The error map generation system is self-sufficient and does not require external reference images or manual intervention. It automatically computes reconstruction errors using only the reconstructed image and available data, enabling precise objective assessment without additional complex equipment or procedures.
3Measurement precision
If comprehensive error detection methods are used, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the error detection process into distinct modular components: error map generation, error quantification, and quality assessment. Each module performs a specific function independently, enabling comprehensive error detection through systematic analysis while keeping individual processing steps manageable and computationally efficient.
Data Source
AI summary
Systems and methods for image processing are provided in the present disclosure. The systems may obtain a first image and a second image associated with a same object; determine a plurality of first blocks of the first image and a plurality of second blocks of the second image, the plurality of second blocks and the plurality of first blocks being in one-to-one correspondence; determine a plurality of first characteristic values based on the plurality of first blocks and the plurality of second blocks; and/or generate a first target map associated with the first image and the second image based on the plurality of first characteristic values.