Multi-Scale Self-Attention Image Processing for Denoising and Upsampling
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Solution Overview
Problem
Existing image processing technologies face challenges in effectively removing noise and artifacts while enhancing image resolution using neural networks, particularly in handling low-quality or low-resolution images.
Innovation Solution
An image processing apparatus utilizing multiple neural networks, including convolutional neural networks, performs self-attention operations to extract and process feature data from images, employing transform blocks and self-attention modules to enhance image quality and resolution through denoising and up-sampling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If deep neural networks are used for image processing to remove noise and artifacts, then image quality is improved, but computational complexity and processing time increase
Solution Approach 1:
The image processing is divided into multiple stages: first feature data extraction, then self-attention operations on different area sizes (first areas with first number of pixels, second areas with second number of pixels greater than the first), and finally second image generation. This segmentation allows the system to process images in manageable steps, reducing the computational burden of any single operation while maintaining overall image quality improvement.
Solution Approach 2:
The patent applies self-attention operations selectively on different area sizes rather than processing the entire image uniformly. By performing self-attention on first areas with fewer pixels and second areas with more pixels, the system applies partial actions at different scales, optimizing computational resources while still achieving comprehensive noise removal and artifact elimination across the entire image.
2Measurement precision
If self-attention operations are performed on larger areas to capture more contextual information, then image processing accuracy is improved, but processing time increases
Solution Approach 1:
The patent segments the self-attention operation into two distinct scales: first self-attention operations on first areas with a first number of pixels, and second self-attention operations on second areas with a second number of pixels greater than the first. This multi-scale segmentation allows the system to capture contextual information at different levels of detail, improving processing accuracy without requiring all operations to process the entire image at maximum resolution.
Solution Approach 2:
The patent introduces a dimensional aspect by operating on areas of different sizes (first number of pixels vs. second number of pixels). This multi-dimensional approach allows the system to capture both fine-grained details from smaller areas and broader contextual information from larger areas, achieving comprehensive accuracy optimization without linearly increasing processing time for a single operation.
Data Source
AI summary
An image processing apparatus for processing an image by using one neural network, includes: a memory storing one instruction; and one processor configured to execute the one instruction to: obtain first feature data, based on a first image, obtain pieces of second feature data corresponding to first areas of the first image by performing first image processing on the first feature data, the first areas comprising a first number of pixels, obtain third feature data, based on the first image, obtain pieces of fourth feature data corresponding to second areas of the first image, by performing second image processing on the third feature data, the second areas comprising a second number of pixels that is greater than the first number, and generate a second image, based on the pieces of second feature data and the pieces of fourth feature data.