Shared Basis Mixture Model for Image Reconstruction
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Solution Overview
Problem
Conventional image reconstruction techniques that utilize image priors are resource-intensive and inefficient, limiting their applicability to only a few computing devices and requiring significant time for processing.
Innovation Solution
A shared basis mixture model is introduced, which learns to model image patch distributions using a Gaussian mixture model with shared basis vectors, reducing computational complexity and improving accuracy by employing an expectation/maximization algorithm to generate efficient and effective image priors for tasks like denoising and deblurring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image reconstruction techniques with image priors are used, then reconstruction quality is improved, but computational resource consumption increases significantly
Solution Approach 1:
The image is divided into multiple patches, and the algorithm processes each patch independently using the shared basis mixture model. This segmentation allows parallel processing and reduces the overall computational burden while maintaining reconstruction quality across the entire image.
Solution Approach 2:
The patent transforms the image reconstruction problem into a parameter estimation problem in the transform domain. By working with transformed coefficients rather than raw pixel values, and by modeling the distribution of these parameters using Gaussian mixture models with shared bases, the computational complexity is reduced while preserving reconstruction accuracy.
2Measurement precision
If conventional image reconstruction techniques are used, then reconstruction quality is improved, but processing time increases significantly
Solution Approach 1:
The shared basis mixture model is pre-computed and stored before the actual reconstruction process. This preliminary action creates a lookup table of common image patterns and their corresponding transformations, which can be quickly applied during reconstruction without performing complex computations in real-time, thus reducing processing time.
Solution Approach 2:
The patent uses a shared basis model that captures common structures across multiple images. This model serves as a template or copy of typical image patterns that can be efficiently applied to reconstruct individual images, avoiding the need to compute from scratch for each image and significantly reducing processing time.
3Measurement precision
If conventional image reconstruction techniques are used, then reconstruction quality is improved, but device complexity increases
Solution Approach 1:
The shared basis mixture model serves multiple functions: it models the distribution of image patches, enables fast reconstruction through pre-computed bases, and works across different imaging scenarios. This universal model reduces algorithmic complexity by providing a single framework that handles various reconstruction tasks without requiring separate complex algorithms for each case.
Data Source
AI summary
An image prior as a shared basis mixture model is described. In one or more implementations, a plurality of image patches are generated from one or more images. A shared basis mixture model is learned to model an image patch distribution of the plurality of image patches from the one or more images as part of a Gaussian mixture model. An image may then be reconstructed using the shared basis mixture model as an image prior.