Style Transfer Network for Image Blending
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Solution Overview
Problem
Existing image processing algorithms fail to deeply blend different images, resulting in unsatisfactory synthesis effects, particularly in smart image pasting applications where different styles are involved, and require different optimization methods for each image type.
Innovation Solution
A pre-trained style transfer network model, comprising an encoder, normalizing network, and decoder, is used to extract and normalize content features of one image based on the style features of another, generating a target image that harmonizes the styles of both, employing iterative training and loss function calculations to adjust weight parameters for improved blending.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing image processing algorithms are used to blend images, then the processing speed is maintained, but the synthesis effect is unsatisfactory and styles are not deeply blended
Solution Approach 1:
The patent segments the image blending process into distinct feature extraction and style transfer stages. The encoder extracts content features from the first image and style features from the second image separately, then the decoder combines them through normalized feature fusion. This segmentation allows each stage to be optimized independently, achieving deep style blending without requiring complete algorithmic redesign.
Solution Approach 2:
The patent applies preliminary action by pre-training the style transfer network model on large datasets before actual image blending. The encoder and decoder are pre-trained to recognize and transfer style features effectively. This preliminary training prepares the model to achieve satisfactory synthesis effects directly during inference without requiring complex post-processing optimization for each image pair.
2Manufacturing precision
If different optimization methods are applied for each image type, then the synthesis effect is improved, but the processing time and complexity increase
Solution Approach 1:
The patent implements universality through a unified style transfer network model that handles multiple image types and styles through a single architecture. The encoder-extractor-decoder framework is designed to be style-agnostic, adapting to different image types (photographs, paintings, sketches, etc.) through feature extraction rather than requiring separate optimization methods for each type. This reduces processing time while maintaining synthesis quality across diverse image types.
Solution Approach 2:
The patent uses parameter changes in the form of learnable weight parameters within the network model to adapt to different image types. Instead of changing the algorithm structure for each image type, the model adjusts its internal parameters during training and inference to optimize feature extraction and style transfer for the specific input images, achieving type-specific optimization without the overhead of multiple specialized algorithms.
3Manufacturing precision
If iterative training with loss function calculations is used to adjust weight parameters, then the blending effect is improved, but the training time increases
Solution Approach 1:
The patent implements feedback through loss function calculations that measure the difference between the generated blended image and the target style reference. The loss function provides gradient feedback to the network model, guiding the adjustment of weight parameters in the encoder and decoder. This feedback mechanism enables the model to learn effective style transfer through iterative optimization, achieving satisfactory blending effects through data-driven parameter adjustment rather than manual tuning.
4Manufacturing precision
If post-processing optimization is applied to each synthesized image, then the synthesis effect is improved, but the overall processing time increases
Solution Approach 1:
The patent applies self-service by designing the style transfer network model to produce high-quality synthesized images directly during the forward propagation phase, eliminating the need for separate post-processing optimization steps. The encoder-extractor-decoder architecture is trained to output well-blended results that satisfy synthesis requirements without requiring additional manual or algorithmic refinement. This self-sufficient approach maintains processing efficiency while achieving satisfactory synthesis effects.
Data Source
AI summary
An image processing method is disclosed. The image processing method may include inputting a first image and a third image to a pre-trained style transfer network model, the third image being a composited image formed by the first image and a second image; extracting content features of the third image and style features of the second image, normalizing the content features of the third image based on the style features of the second image to obtain target image features, and generating a target image based on the target image features and outputting the target image by using the pre-trained style transfer network model.