Two-Stage Image Conversion Model with Adaptive Weight Fusion
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Solution Overview
Problem
Conventional image processing methods often result in image distortion due to mismatched texture synthesis during feature conversion, leading to suboptimal image conversion performance.
Innovation Solution
An image processing method involving a two-stage image conversion model and weight prediction model, where a first stage model performs preliminary conversion and a second stage model refines detail features, with weight matrices used for adaptive fusion of intermediate images to produce a target image, leveraging machine learning models like convolutional neural networks and cycle consistency constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If texture synthesis is used for image feature conversion, then image conversion can be achieved, but image distortion occurs due to mismatch
Solution Approach 1:
The patent segments the image conversion process into two distinct stages: a first stage conversion model that performs preliminary feature conversion, and a second stage conversion model that refines the conversion results. This segmentation allows each model to specialize in different aspects of the conversion process, reducing mismatch and distortion while maintaining conversion capability.
Solution Approach 2:
The patent introduces intermediate images as mediators between the input and final output images. The first stage model generates intermediate images that capture preliminary conversion features, which then serve as input for the second stage model. This intermediary approach enables gradual transformation and reduces direct mapping errors that cause distortion.
2Device complexity
If single-stage conversion model is used, then processing is simple, but conversion performance is suboptimal
Solution Approach 1:
The patent divides the conversion system into two separate conversion models operating in sequence. The first stage model handles coarse feature transformation, while the second stage model focuses on refining details and correcting mismatches. This segmentation improves conversion reliability without requiring excessively complex single-model architectures.
Solution Approach 2:
The first stage conversion model performs preliminary feature transformation before the second stage model refines the results. This preliminary action allows the system to establish the basic conversion framework first, then progressively improve the quality, achieving better performance than a single-stage approach.
Data Source
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AI summary
An image processing method, comprising: acquiring an image to be processed belonging to a first image class; inputting the image to be processed into a first-stage image conversion model to obtain a first intermediate image; converting the first intermediate image into a second intermediate image by means of a second-stage image conversion model; determining a second weight matrix corresponding to the second intermediate image; determining a first weight matrix corresponding to the first intermediate image; and carrying out fusion on the first intermediate image and the second intermediate image according to the corresponding first weight matrix and second weight matrix to obtain a target image corresponding to the image to be processed and belonging to a second image class.