Super-Resolution Image Processing Model Using Pre-Trained Neural Networks
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Solution Overview
Problem
Existing super-resolution processing methods for images are inefficient in providing high-quality, high-definition images quickly, as they rely on interpolation and mapping relationships between low-resolution and high-resolution images, which can be computationally intensive and less accurate.
Innovation Solution
A convolutional neural network-based image processing model is generated, including an input layer, intermediate layer, and output layer, to perform super-resolution processing by updating convolution kernel parameters based on training images, allowing for efficient generation of high-resolution images from low-resolution inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If interpolation processing is used to reduce image resolution, then low-resolution images can be obtained from high-resolution images, but the processing efficiency is low and computational complexity is high
Solution Approach 1:
The patent pre-generates multiple groups of corresponding low-resolution and high-resolution images through interpolation processing during the training phase. This preliminary preparation creates a mapping relationship database that can be quickly queried during actual super-resolution processing, avoiding the need to perform interpolation calculations in real-time and thus significantly improving processing efficiency
2Measurement precision
If mapping relationship learning between low-resolution and high-resolution images is performed through machine learning, then super-resolution processing can be achieved, but the training complexity and computational resources required are high
Solution Approach 1:
The patent segments the super-resolution processing into two distinct phases: (1) a training phase where mapping relationships are learned from pre-prepared image pairs, and (2) an inference phase where the trained model is applied. This segmentation allows complex training to be performed offline, while online processing becomes simpler and faster, effectively reducing the perceived training complexity for end users
Solution Approach 2:
The patent performs the computationally intensive work of generating training data and establishing mapping relationships in advance during the training phase. By preparing everything needed for super-resolution processing beforehand, the actual application phase requires minimal computational resources and time, making the system more practical for real-world deployment
3Manufacturing precision
If conventional super-resolution methods are used, then image resolution can be improved, but the processing time is long and cannot provide high-definition images quickly
Solution Approach 1:
The patent pre-trains the neural network model with大量 paired low-resolution and high-resolution images, establishing robust mapping relationships during offline training. When actual super-resolution processing is needed, the pre-trained model can directly infer high-resolution images from low-resolution inputs without requiring time-consuming calculations, thus achieving both high quality and fast processing speed
Solution Approach 2:
The patent replaces traditional mechanical interpolation-based super-resolution methods with a neural network-based learning approach. The neural network learns complex non-linear mappings between low and high resolution images, providing superior quality results compared to conventional methods while the pre-trained model enables fast inference that overcomes the speed limitations of traditional approaches
Data Source
AI summary
A method and apparatus, device, and storage medium for generating an image processing model and processing images based on the generated image processing model. The method includes generating an initial model for an image resolution processing, the initial model comprising an input layer, an output layer, and an intermediate layer; obtaining a training image comprising a first image and a second image, the first image being obtained by performing a resolution reduction processing on the second image; inputting image data of the first image into the intermediate layer from the input layer for a convolution calculation, and obtaining result data based on the convolution calculation, the result data comprising channel output data of N*N channels; and performing a parameter update on convolution kernel parameters in the intermediate layer based on the result data and the second image, and generating the image processing model according to the initial model.