Image Stylization via Feature Correction Maps
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Solution Overview
Problem
Existing image stylization methods often result in non-ideal output pictures when attempting to combine images with great visual differences, as the content of one image significantly affects the style of the output, and vice versa.
Innovation Solution
An image processing method using a convolutional neural network to extract feature maps from both the original and style reference images, calculate correction values based on Euclidean distances between feature blocks, and apply these corrections through a deconvolution process to maintain the content of the original image while adding the style of the reference image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional image stylization methods are used to combine images with great visual differences, then the style transfer process is simple, but the content of one image significantly affects the style of the output and vice versa, resulting in non-ideal output pictures
Solution Approach 1:
The patent segments the image processing into distinct feature extraction and feature combination stages using separate neural network components (convolutional network for content features, generative adversarial network for style features). This segmentation allows independent optimization of each stage, improving stylization quality while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary feature map that mediates between content and style features. The feature map serves as a bridge that combines content features from the original image with style features from the reference image through controlled feature interaction, preventing direct contamination between content and style while achieving ideal stylization output.
2Adaptability or versatility
If the content of the style reference picture is allowed to affect the output picture, then the style transfer is more natural, but the final output picture does not achieve ideal stylization效果
Solution Approach 1:
The patent applies local quality control by allowing style reference picture content to affect only specific regions of the output picture where style enhancement is needed, while preserving the original content structure through spatially selective feature combination. This is achieved through the feature map mechanism that locally integrates style features without globally contaminating content.
Solution Approach 2:
The patent dynamically adjusts the influence parameters of the style reference picture content based on regional needs. By controlling the weighting and integration strength of style features in different spatial locations and feature channels, the system achieves adaptability where style transfer is natural in appropriate regions while maintaining stylization accuracy in content-critical regions.
Data Source
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AI summary
At least one embodiment of the present disclosure provides an image processing method, including: inputting (S11) an original image and a style reference image into a convolutional neural network to generate a first feature map of the original image and a second feature map of the style reference image; calculating (S12) a correction value of any pixel point in the first feature map based on the second feature map; replacing (S13) an original value of each pixel point in the first feature map with a corresponding corrected value to obtain a third feature map; and performing (S14) a deconvolution process on the third feature map to obtain a processed image. An apparatus and a storage medium are further provided.