Deconvolution Kernel Gradient Operator Regularization
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Solution Overview
Problem
Existing deconvolution technologies, such as Wiener filtering, suffer from noise-induced ringing effects that degrade the quality of recovered sharp images due to their reliance on signal-to-noise ratio (SNR) as a regularization constraint.
Innovation Solution
A deconvolution kernel is determined based on a gradient operator and a convolution kernel, using the gradient operator as a regularization constraint to prevent noise interference and improve image recovery quality, specifically through the introduction of singular value decomposition to decompose the deconvolution kernel into one-dimensional kernels for efficient computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If Wiener filtering with SNR-based regularization constraint is used for deconvolution, then the computation is simplified, but noise-induced ringing effects occur that degrade image recovery quality
Solution Approach 1:
The patent changes the regularization constraint parameter from SNR (signal-to-noise ratio) to gradient operator. This parameter substitution fundamentally alters the deconvolution approach, replacing the noise-sensitive SNR constraint with a gradient-based constraint that promotes piecewise smooth solutions. The gradient operator constraint is formulated as minimizing the L2-norm of image gradients, which effectively suppresses noise-induced ringing while preserving edges, thus resolving the contradiction between computational simplicity and image recovery quality.
2Manufacturing precision
If gradient operator is introduced as regularization constraint instead of SNR, then noise interference is prevented and image recovery quality improves, but computational complexity increases
Solution Approach 1:
The patent applies segmentation by decomposing the two-dimensional deconvolution problem into multiple one-dimensional subproblems. The gradient operator constraint is separated into horizontal and vertical gradient components, allowing the optimization to be performed independently in each dimension. This segmentation reduces the overall computational complexity while maintaining the quality improvements achieved through gradient-based regularization.
3Productivity
If deconvolution kernel is decomposed into one-dimensional kernels, then computational efficiency is improved, but the accuracy of image recovery may be affected
Solution Approach 1:
The patent segments the deconvolution kernel into separable one-dimensional kernels along horizontal and vertical directions. This segmentation allows the computationally intensive two-dimensional convolution operation to be replaced by sequential one-dimensional convolutions, significantly improving efficiency. The gradient operator constraint ensures that this segmentation does not compromise recovery accuracy by maintaining the piecewise smooth property in both dimensions independently.
Solution Approach 2:
The patent merges the deconvolution operation with the gradient-based regularization constraint into a unified optimization framework. By combining these two functions (deconvolution and regularization) into a single energy minimization problem, the patent ensures that the separable kernel approximation maintains accuracy while achieving computational efficiency through the coordinated action of both components.
Data Source
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AI summary
The present invention discloses a method for generating a sharp image based on a blurry image. The method includes: acquiring pixel values of pixels in the blurry image, and a convolution kernel of the blurry image; determining a deconvolution kernel of the blurry image based on a preset image gradient operator and the convolution kernel; determining pixel values of pixels in the sharp image based on the deconvolution kernel and the pixel values of the pixels in the blurry image; and generating the sharp image based on the pixel values of the pixels in the sharp image. A deconvolution kernel in the embodiments of the present invention is determined based on a gradient operator and a convolution kernel; in other words, the deconvolution kernel introduces the gradient operator as a regularization constraint, which prevents noise from affecting an image recovery process, and improves the quality of a recovered sharp image.