Multimodal Image Super-Resolution With Convolutional Dictionary Learning
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Solution Overview
Problem
Existing multi-modal image super-resolution techniques face challenges due to translation-invariant dictionaries that lose signal structure and require large training datasets and computational resources, impacting the quality of reconstructed images.
Innovation Solution
A method and system using convolutional dictionaries that are translation-invariant, initialized with sparse coefficients, and iteratively trained to model cross-modal dependencies, reducing the need for extensive training data and computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If translation-invariant dictionaries are used for multi-modal image super-resolution, then the quality of reconstructed images is improved, but the training data requirements and computational resources increase
Solution Approach 1:
The patent segments the image processing task into patch-level operations. Instead of processing entire images globally, the method divides images into local patches and processes them independently through the convolutional dictionary. This segmentation allows the system to achieve translation invariance without requiring extensive training data, as each patch is processed with the same convolutional filters regardless of its position in the image.
Solution Approach 2:
The patent changes the fundamental parameter of dictionary representation from traditional non-translation-invariant bases to convolutional dictionaries with translation invariance. This parameter change enables the system to maintain consistent feature representation across different image regions, improving reconstruction quality while reducing the need for large training datasets through efficient parameter sharing.
2Manufacturing precision
If translation-invariant dictionaries are used for multi-modal image super-resolution, then the quality of reconstructed images is improved, but the computational resources required increase
Solution Approach 1:
The convolutional dictionaries learned through the joint training process serve multiple functions simultaneously: they perform feature extraction, enable translation-invariant representation, and facilitate super-resolution reconstruction. This multi-functionality reduces the need for separate processing stages, thereby improving reconstruction quality while managing computational resource requirements efficiently.
Solution Approach 2:
The patent performs preliminary learning of convolutional dictionaries during a training phase using joint training on multi-modal image pairs. This preliminary action pre-computes the translation-invariant feature representations, so that during actual super-resolution processing, the system can directly apply these pre-learned dictionaries without performing complex optimization in real-time, thus improving reconstruction quality while controlling computational resources during deployment.
3Quantity of substance
If convolutional dictionaries are used instead of traditional dictionaries, then the need for extensive training data is reduced, but the complexity of the learning process increases
Solution Approach 1:
The patent merges the learning of multiple dictionaries into a single joint training process. Instead of separately learning dictionaries for different modalities or different processing stages, the method combines them into a unified convolutional dictionary learning framework that processes multi-modal image pairs simultaneously. This merging reduces training data requirements through shared feature representations while managing complexity through a cohesive algorithmic structure.
Data Source
AI summary
This disclosure relates generally to the field of image processing, and, more particularly, to a method and system for Multimodal Image Super-Resolution (MISR) using convolutional dictionary learning. Existing sparse representation learning based techniques for MISR have certain limitations which impact quality of the reconstructed image. The present disclosure performs MISR using convolutional dictionaries which are translation invariant. Low-resolution image of the target modality and high-resolution image of the guidance modality are modelled using their respective convolutional dictionaries and associated coefficients. Additionally, two coupling convolutional dictionaries are learned to model the relationship between them and synthesize the high-resolution image of the target modality more efficiently.