Image Deblurring Using Offset Features for Spatially Variant Blur
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Solution Overview
Problem
Existing image restoration methods struggle to effectively deblur images with varying blur patterns, particularly those involving motion blur, due to the complexity of handling spatially-variant blur kernels and the resource-intensive nature of deep learning-based neural networks.
Innovation Solution
A method and apparatus utilizing a neural network-based encoding model and implicit function model to generate feature representations and offset information, enabling the deblurring of images by processing spatially-variant blur points through a combination of encoding and implicit function models, which are trained to minimize the difference between blurred and clear images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning-based neural networks are used for image restoration, then deblurring performance is improved, but computational resource requirements increase
Solution Approach 1:
The patent segments the image restoration task into two distinct models: an encoding model that processes the input blurred image to extract features and offset information, and an implicit function model that performs the actual deblurring. This segmentation allows each model to be optimized for its specific function, reducing overall computational requirements while maintaining deblurring performance.
Solution Approach 2:
The patent introduces an intermediary representation consisting of features and offset information that bridges the encoding model and the implicit function model. This intermediary structure enables the system to process spatially-variant blur patterns efficiently by representing the blur kernel characteristics in a compact form, reducing the computational burden on the implicit function model.
2Measurement precision
If spatially-variant blur kernels are handled accurately, then deblurring quality is improved, but device complexity increases
Solution Approach 1:
The patent applies local quality by using the encoding model to extract spatially-variant features and offset information specific to different regions of the image. The implicit function model then uses these localized representations to perform deblurring adapted to local blur characteristics, achieving high deblurring quality without requiring a single complex model to handle all spatial variations.
Solution Approach 2:
The patent changes the parameter representation by using offset information to describe spatially-variant blur kernels rather than directly processing the full kernel matrices. This parameter transformation simplifies the representation of complex spatially-variant blur patterns, reducing model complexity while maintaining accurate deblurring capability.
Data Source
AI summary
An image deblurring method and apparatus are provided. The image deblurring method includes generating a primary feature representation on a first blur point in an input image and offset information on similar points of the first blur point by encoding the input image by implementing an encoding model, generating secondary feature representations on the similar points by applying the offset information to the primary feature representation, and generating an output image, based on the secondary feature representations and the offset information, by implementing an implicit function model.