Image Super-Resolution with Element-Unshuffled Downsampling
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Solution Overview
Problem
Existing deep CNN-based image super-resolution models require massive computations and are inefficient for deployment on mobile devices due to high resource and power consumption, despite the potential benefits of downsampling operations for improving performance.
Innovation Solution
The introduction of a Hybrid Element-Unshuffled Network (HEUN) that incorporates element-unshuffled downsampling and Self-Residual Depthwise Separable Convolutions, reducing parameters and computation costs while maintaining high performance through an element-unshuffle operation and max-pooling with group convolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep CNN-based image super-resolution models are used, then reconstruction performance is improved, but computation cost and resource consumption increase
Solution Approach 1:
The patent segments the feature processing into distinct pathways: element-unshuffled downsampling for global feature extraction, max-pooling for local feature aggregation, and group convolution for refined feature transformation. This segmentation allows each component to operate efficiently on specialized data, reducing overall computation cost while maintaining reconstruction performance.
Solution Approach 2:
The patent introduces element-unshuffled downsampling that operates in the channel dimension rather than spatial dimensions, transforming the feature representation efficiently. This dimensional transformation reduces computation by processing features in a more compact form before reconstruction, addressing the contradiction between performance and computation cost.
2Productivity
If downsampling operations are applied, then computation efficiency is improved, but image quality may deteriorate
Solution Approach 1:
The patent applies different processing qualities to different feature levels: element-unshuffled downsampling processes global features with reduced computation, while max-pooling and group convolution preserve local feature quality. This local quality principle ensures that computation efficiency gains from downsampling do not compromise the quality of critical local image details.
Solution Approach 2:
The patent creates a composite feature representation by combining results from element-unshuffled downsampling, max-pooling, and group convolution. This composite approach integrates the computational efficiency of downsampling with the quality preservation of pooling and convolution operations, achieving both improved efficiency and maintained image quality.
3Use of energy by moving object
If model complexity is reduced for mobile deployment, then resource consumption is improved, but reconstruction performance may worsen
Solution Approach 1:
The element-unshuffled downsampling operation is self-service in that it efficiently transforms features without requiring heavy computational resources, making the model suitable for mobile deployment. This self-efficient operation reduces resource consumption while maintaining the reconstruction performance needed for practical application.
Solution Approach 2:
The patent changes the parameters of the downsampling operation from traditional spatial downsampling to element-unshuffled downsampling in the channel dimension. This parameter change reduces the computational complexity and resource consumption of the model while preserving the reconstruction performance, enabling efficient mobile deployment.
Data Source
AI summary
A system and corresponding method perform image super-resolution (SR). The system comprises an element-unshuffled downsampler and an image SR module. The image SR module performs image SR on a low-resolution (LR) representation of a high-resolution (HR) original image. The HR original image is at a higher resolution relative to a resolution of the LR representation. The image SR module produces a reconstructed version of the HR original image via the image SR performed. The image SR is based on element-unshuffled downsampling of the LR representation. The element-unshuffled downsampler performs the element-unshuffled downsampling. The image SR module outputs the reconstructed version produced. The system performs the image SR with fewer parameters and less computation cost relative to conventional image SR.