Convolution Deconvolution Neural Network for Image Resolution
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Solution Overview
Problem
Current image super-resolution methods face challenges in efficiently enhancing the resolution of low-quality images, particularly in computational complexity and reconstruction accuracy, especially when scaling up images from low-resolution inputs.
Innovation Solution
A convolution-deconvolution neural network is employed to filter and upscale images, comprising a first convolution layer for feature extraction, a deconvolution layer for resolution improvement, and a second convolution layer for constructing high-resolution images, with a training process that adjusts weights based on reconstruction errors to achieve improved image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image super-resolution methods are used to upscale images, then image resolution is improved, but computational complexity increases significantly
Solution Approach 1:
The patent segments the image processing task into multiple convolutional layers, each performing specific feature extraction operations. This divides the complex super-resolution problem into manageable stages, reducing the computational burden of any single operation while maintaining overall resolution improvement.
Solution Approach 2:
The patent transforms the image processing from spatial domain operations to feature space operations through convolutional filtering. By mapping images to feature maps and processing in this transformed dimension, the method achieves resolution enhancement with reduced computational complexity compared to direct spatial domain upscaling.
2Loss of information
If conventional filtering methods are applied to extract image features, then feature extraction is achieved, but image quality and reconstruction accuracy deteriorate
Solution Approach 1:
The patent implements feedback mechanisms through the sequential processing architecture where later convolutional layers refine features extracted by earlier layers. The multi-stage feature extraction and reconstruction process allows iterative improvement, where reconstruction errors from later stages feed back into refining earlier feature representations, thereby maintaining both image quality and reconstruction accuracy.
Solution Approach 2:
The patent performs preliminary feature extraction and filtering operations in early convolutional layers before the main reconstruction process. By pre-processing and organizing image features in advance through multiple filtering stages, the system prepares optimized feature representations that improve subsequent reconstruction accuracy while preserving image quality.
Data Source
AI summary
A method and system for processing an image operates by: filtering a first real image to obtain a first feature map therefor with performances of image features improved; upscaling the obtained first feature map to improve a resolution thereof, the feature map with improved resolution forming a second feature map; and constructing, from the second feature map, a second real image having enhanced performances and a higher resolution than that of the first real image.