Multi-Scale Residual Neural Network for Image Restoration
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Solution Overview
Problem
Existing image processing technologies face challenges in restoring high-resolution images from low-resolution images captured by multilens arrays or single lenses, particularly due to lens aberrations and structural disparities, which result in image degradation and blur.
Innovation Solution
A neural network-based method utilizing a series of residual blocks that perform image processing on different scales through skip connections, normalization, and pixel shuffling to gradually restore images, reducing degradation and enhancing resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a neural network with multi-scale residual blocks is used for image restoration, then image restoration performance and resolution are improved, but device complexity and computational requirements increase
Solution Approach 1:
The neural network is segmented into multiple residual blocks, each handling specific scale transformations. The network processes images at multiple scales (1x, 2x, 4x) through separate processing paths, dividing the complex restoration task into manageable scale-specific operations that can be independently optimized and executed
Solution Approach 2:
The patent introduces a scale dimension to the traditional single-scale image restoration approach. By processing images at multiple scaling factors (1x, 2x, 4x) simultaneously through different processing paths and combining results, the system adds a dimensional aspect to the restoration process that improves performance without requiring proportionally increased complexity
2Reliability
If multiple scales are processed in each residual block, then image quality across various scales is maintained, but processing time and computational load increase
Solution Approach 1:
The network performs preliminary scaling operations at the beginning of each processing path (1x, 2x, 4x scales) before main restoration processing. This preliminary action prepares the data in advance for subsequent processing stages, allowing parallel execution of scale-specific operations and reducing overall processing time through efficient pipeline utilization
Solution Approach 2:
Results from multiple scale processing paths (1x, 2x, 4x) are merged through element-wise addition in the residual blocks. This merging operation combines the benefits of multi-scale processing into a single unified output, maintaining image quality consistency across scales while avoiding the need for separate processing sequences that would increase time consumption
Data Source
AI summary
A method for processing an image by using a neural network is provided. The method includes obtaining a target image; and obtaining a restored image corresponding to the target image by using the neural network, the neural network including a plurality of residual blocks connected sequentially. The obtaining the restored image includes obtaining the restored image by performing, in each residual block of the plurality of residual blocks, image processing on an input image, which is input from a previous residual block, on different scales, and providing results of the image processing performed on the different scales to a subsequent residual block.