Image Processing Iterative Reconstruction for Resolution and Artifacts
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Solution Overview
Problem
Conventional image processing techniques for reconstructing superresolution images are limited by factors such as signal to noise ratio (SNR), physical optics limits, and artifacts, resulting in low reconstruction speed and image quality.
Innovation Solution
A method and system for image processing that generates a preliminary image by filtering image data, then performs iterative operations based on objective functions to produce an intermediate and target image, incorporating terms for image difference, continuity, and sparsity to enhance resolution and contrast.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional image processing techniques are used for image reconstruction, then the reconstruction process is simple, but the reconstructed images have artifacts and low resolution due to SNR limits and physical optics constraints
Solution Approach 1:
The reconstruction process is segmented into multiple iterative steps: generating preliminary images from raw data, computing intermediate images with sparsity constraints, and producing final super-resolution images. This multi-stage segmentation allows each step to optimize specific aspects of image quality while managing computational complexity
Solution Approach 2:
Preliminary images are generated in advance from raw image data before the main reconstruction process. These preliminary images serve as initial estimates that guide the subsequent iterative reconstruction, improving convergence speed and final image quality while reducing artifacts
2Productivity
If conventional image processing techniques are used, then the processing method is straightforward, but the reconstruction speed is relatively low
Solution Approach 1:
The patent transforms the image reconstruction problem into a parameter optimization problem by defining objective functions with multiple terms (sparsity term, data fidelity term, etc.). By adjusting these parameters and their weights iteratively, the system achieves faster convergence to high-quality solutions while managing algorithmic complexity through structured optimization
Solution Approach 2:
The patent replaces traditional mechanical/optical image enhancement methods with computational algorithms. Instead of relying on physical optics limits or simple filtering, the system uses iterative mathematical optimization with sparsity constraints and objective functions to achieve super-resolution, dramatically improving reconstruction speed and quality
3Manufacturing precision
If iterative operations with multiple objective function terms are performed, then image resolution and contrast are improved, but the computational complexity increases
Solution Approach 1:
The complex objective function is segmented into multiple distinct terms (sparsity term L1 norm, data fidelity term L2 norm, continuity term with Hessian matrix). Each term addresses a specific aspect of image quality, allowing the optimization process to systematically improve resolution and contrast while maintaining manageable computational complexity through modular term processing
Solution Approach 2:
Preliminary images are computed in advance to serve as initial estimates for the iterative optimization. This preliminary action provides a good starting point that reduces the number of iterations needed to converge to high-quality solutions, thereby improving resolution and contrast while limiting the increase in computational complexity
Data Source
AI summary
Systems and methods for image processing are provided in the present disclosure. The systems may generate a preliminary image by filtering image data generated by an image acquisition device. The system may generate an intermediate image by performing, based on a first objective function, a first iterative operation on the preliminary image. The first objective function may include a first term associated with a first difference between the intermediate image and the preliminary image, a second term associated with continuity of the intermediate image and a third term associated with sparsity of the intermediate image. The systems may also generate a target image by performing, based on a second objective function, a second iterative operation on the intermediate image. The second objective function may be associated with a system matrix of the image acquisition device and a second difference between the intermediate image and the target image.