Image Super-Resolution with Attention-Based Texture Transfer
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Solution Overview
Problem
Conventional image super-resolution methods face challenges in accurately transferring and preserving texture features, leading to blurry and distorted results, particularly in single-image-based and reference image-based approaches.
Innovation Solution
A multi-level attention mechanism is employed to determine relevance between pixel blocks of input and reference images based on texture features, using a learnable texture extractor and a combination of hard and soft attention mechanisms to enhance texture transfer accuracy, and a cross-scale feature integration module to fuse features across different resolutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional single-image-based or reference image-based super-resolution methods are used, then the processing speed and simplicity are maintained, but the texture features become blurry and distorted
Solution Approach 1:
The patent divides the image into multiple pixel blocks and processes each block separately through the attention mechanism. This segmentation allows the system to focus on local texture features independently, improving texture accuracy without requiring complex global processing. The image is split into manageable units that can be handled by the neural network efficiently.
Solution Approach 2:
The patent introduces an attention mechanism as an intermediary component between the input image and the output super-resolution image. This attention mechanism acts as a mediator that selectively emphasizes important texture features while suppressing less relevant information, thereby improving texture accuracy without directly increasing the overall system complexity.
2Reliability
If texture features are transferred from reference images, then the realism and clarity of the output image improve, but the risk of texture distortion and blur increases
Solution Approach 1:
The patent implements a feedback mechanism through the attention model that continuously adjusts the weighting of different pixel blocks based on their relevance to the current processing position. This feedback loop allows the system to correct potential distortions by dynamically adapting to the actual texture patterns observed, ensuring accurate texture transfer while minimizing blur and distortion effects.
Solution Approach 2:
The patent dynamically changes the attention weights as parameters based on the position and content of pixel blocks being processed. By adjusting these parameters adaptively rather than using fixed weights, the system can accurately transfer textures while avoiding the blur and distortion that would result from uniform or static parameter application across different image regions.
3Measurement precision
If pixel-level texture feature processing is used, then the detail accuracy improves, but the computational complexity and processing time increase significantly
Solution Approach 1:
The patent segments the image into pixel blocks and processes them in a structured manner through the attention mechanism. This segmentation approach maintains high detail accuracy by treating each block individually while improving processing efficiency through the hierarchical and modular nature of the attention model, avoiding the need to process every pixel independently.
Solution Approach 2:
The patent applies partial action by focusing the attention mechanism only on relevant pixel blocks rather than processing the entire image uniformly. The attention model selectively applies computational resources to areas where texture detail is most important, achieving high measurement precision while maintaining productivity by avoiding unnecessary processing of less critical regions.
Data Source
AI summary
There is provided a solution for image processing. In this solution, first and second information is determined based on texture features of an input image and a reference image. The first information at least indicates for a first pixel block in the input image a second pixel block in the reference image most relevant to the first pixel block in terms of the texture features, and the second information at least indicates a relevance of the first pixel block to the second pixel block. A transferred feature map with a target resolution is determined based on the first information and the reference image. The input image is transformed into an output image with the target resolution based on the transferred feature map and the second information. The output image reflects a texture feature of the reference image.