Super-Resolution Neural Network Residual Feedback Loop
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Solution Overview
Problem
Existing super-resolution image generation methods using standard interpolation algorithms often fail to capture detailed features of the original image, resulting in a lack of image fidelity in the super-resolution output.
Innovation Solution
The proposed method involves an image processing system that uses an upscaling convolutional network to generate a feature image, followed by a downscaling convolutional network to produce a downscaled feature image. A residual image is then determined between the downscaled and original feature images, upscaled, and used to correct the original feature image, ultimately generating a high-quality super-resolution image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Length of stationary object
If standard interpolation algorithms are used to generate super-resolution images, then the image resolution is increased, but the detailed features of the image are lost
Solution Approach 1:
The patent implements a feedback mechanism by downsampling the upscaled feature image and comparing it with the original feature image to generate a residual image. This residual information is then fed back through an upsampling network and added to the original upscaled features, creating a closed-loop system that continuously refines the super-resolution output to preserve detailed features.
Solution Approach 2:
The patent introduces an intermediary residual image that captures the difference between the downscaled and original feature images. This residual image serves as a mediator that carries detailed feature information, which is then upscaled and integrated with the main upscaled features to enhance the final super-resolution image quality.
2Manufacturing precision
If deep Laplacian Pyramid Super-Resolution Network is used, then image super-resolution is achieved, but the computational complexity increases
Solution Approach 1:
The patent segments the super-resolution task into distinct functional modules: an upsampling network for initial resolution enhancement, a downsampling network for feature extraction, a residual network for detail enhancement, and a synthesis network for final image generation. This modular segmentation reduces overall computational complexity while maintaining high image quality.
Solution Approach 2:
The patent applies partial action by focusing computational resources on specific aspects of image enhancement. Instead of processing the entire image uniformly through multiple complex stages, the residual network selectively processes only the difference information (residuals) that contain the most important detailed features, reducing unnecessary computations.
Data Source
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AI summary
The present disclosure relates to an image processing method. The image processing method may include upscaling a feature image of an input image by an upscaling convolutional network to obtain a upscaled feature image; downscaling the upscaled feature image by a downscaling convolutional network to obtain a downscaled feature image; determining a residual image between the downscaled feature image and the feature image of the input image; upscaling the residual image between the downscaled feature image and the feature image of the input image to obtain an upscaled residual image; correcting the upscaled feature image using the upscaled residual image to obtain a corrected upscaled feature image; and generating a first super-resolution image based on the input image using the corrected upscaled feature image.