Self-Similarity Image Processing for Echo Effect Compensation
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Solution Overview
Problem
Conventional image processing methods, such as those using Gradient Index Lens Arrays, suffer from the 'echo effect' due to out-of-focus images being processed by multiple lenses, leading to aberrations that are difficult to correct, especially in the presence of noise, and require complex computational iterative algorithms.
Innovation Solution
A self-similarity technique is employed to process images by generating a processing algorithm based on self-similarity assumptions, involving parameter mapping operators to alter and correct degraded images in a simple, fast, and non-iterative manner, effectively compensating for imperfections and producing higher quality images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional de-convolution methods (Fourier division or iterative algorithms) are used to correct echo effects, then image quality can be improved, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent transforms the de-convolution problem from the spatial domain to the frequency domain by applying Fourier transforms. This parameter transformation allows the complex iterative de-convolution process to be replaced by simple algebraic operations in the frequency domain, significantly reducing computational complexity while maintaining image quality improvement.
Solution Approach 2:
The patent extracts and removes the echo kernel parameters from the degraded image processing. By identifying and isolating the echo effect characteristics (kernel parameters), the method can directly compensate for these specific distortions without requiring complex iterative algorithms to handle the entire image processing, thus simplifying the computational burden.
2Manufacturing precision
If Fourier division method is used for de-convolution, then de-echoing can be achieved, but the solution becomes unstable in the presence of noise
Solution Approach 1:
The patent applies regularization techniques before performing the Fourier division to cushion against the instability caused by noise. By introducing prior knowledge constraints and regularization terms in the frequency domain processing, the method prevents amplification of noise during the de-convolution process, ensuring stable and reliable results even in noisy conditions.
3Manufacturing precision
If iterative algorithms are implemented for de-convolution, then accurate image restoration is possible, but processing time and computational resources increase substantially
Solution Approach 1:
The patent replaces the mechanical iterative computational process with a direct frequency domain solution. Instead of repeatedly applying complex iterative algorithms in the spatial domain, the method substitutes this with a single-pass Fourier transform-based de-convolution in the frequency domain, achieving the same restoration accuracy with dramatically reduced processing time and computational resource requirements.
Data Source
AI summary
A method and system for utilizing a self-similarity technique to process an image is disclosed. In accordance with embodiments of the present invention, an image is processed utilizing an image processing technique based on a self-similarity assumption. Through the use of the method and apparatus in accordance with the present invention, imperfections that are present in a degraded image can be compensated for in a simple, fast and non-iterative fashion thereby resulting in a higher quality image. A first aspect of the present invention is a method for utilizing a self-similarity technique to process an image. The method includes obtaining a corrupted image, altering the corrupted image to obtain an altered image, determining a plurality of parameters of a parametric mapping operator for mapping the altered image into the corrupted image and utilizing the plurality of parameters to map the corrupted image into an output image.