Deep Learning Super-Resolution Network with Residual Blocks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing super-resolution image reconstruction methods using convolutional neural networks are computationally inefficient and expensive due to their large number of residual blocks and subcomponents, leading to high computational complexity and memory consumption.
Innovation Solution
A simplified deep learning-based method employing serially connected residual blocks with integrated Laplacian deep super-resolution models and reduced feature maps, utilizing fewer dense residual Laplacian modules and convolutional layers to achieve clearer images with lower computational complexity and faster reconstruction speeds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of residual blocks and subcomponents are used for image super-resolution reconstruction, then image reconstruction accuracy is improved, but computational complexity and hardware cost increase
Solution Approach 1:
The network is divided into multiple residual blocks with skip connections, where each block processes a specific portion of the feature extraction task. This segmentation allows the model to achieve high reconstruction accuracy through collaborative processing of multiple simplified units rather than one complex unit.
Solution Approach 2:
Residual blocks are nested within each other in a sequential architecture, where each block builds upon the features extracted by previous blocks. The nested structure enables progressive refinement of image features while maintaining computational efficiency through reusable block designs.
2Measurement precision
If multiple residual blocks and subcomponents are used for super-resolution, then reconstruction quality improves, but inference time increases
Solution Approach 1:
The network performs preliminary feature extraction in early residual blocks before processing continues in subsequent blocks. This preliminary action captures essential image features efficiently, allowing the model to achieve good reconstruction quality without requiring all residual blocks to process every detail, thus reducing overall inference time.
Solution Approach 2:
Skip connections enable continuous flow of feature information through residual blocks, allowing parallel computation and reducing the sequential processing time. The continuous action of feature propagation across blocks maintains reconstruction quality while enabling more efficient time utilization through parallelizable operations.
3Measurement precision
If densely connected residual blocks with Laplacian attention network are used, then image super-resolution accuracy improves, but memory consumption increases
Solution Approach 1:
The Laplacian attention mechanism applies local quality enhancement by selectively processing specific regions of the feature maps based on local variance and edge information. This localized approach maintains high super-resolution accuracy in critical regions while reducing overall memory consumption by avoiding uniform processing of entire feature maps.
Solution Approach 2:
The model dynamically adjusts the number of residual blocks and feature map dimensions based on input image resolution and complexity. This parameter change strategy allows the network to allocate memory resources efficiently, using more computational resources for high-resolution inputs and fewer resources for low-resolution inputs, thereby optimizing the accuracy-memory trade-off.
Data Source
AI summary
A deep learning network can have the following elements arranged in sequence: a convolutional block, a first residual block (RB), a first element-wise adder, a second RB, a second element-wise adder, and an upsampling unit. At least one processor can perform processing comprising generating a super resolution image by processing, with the deep learning network, an image having a resolution lower than a resolution of the super resolution image.