Image Expansion Padding for Color and Style Consistency
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Solution Overview
Problem
Existing image expansion methods generate expanded regions with low matching quality to the original image, often exhibiting color discrepancies and style inconsistencies.
Innovation Solution
An image processing method involving a padding operation, training a model with a training dataset that includes cropped images from original images using random masks, and performing image expansion based on predicted noise to enhance consistency and reduce color differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing image expansion methods are used, then image expansion can be performed, but the matching quality between expanded region and original image is low
Solution Approach 1:
The patent applies preliminary action by performing a padding operation on the original image before expansion. The original image is padded with a background to create a larger canvas, ensuring that the expanded regions will seamlessly integrate with the original image boundaries. This pre-processing step establishes proper boundaries and context before the actual expansion occurs, preventing color discrepancies at the edges.
Solution Approach 2:
The patent utilizes parameter changes by training the model on a specialized dataset that includes cropped images at various scales and positions. The training process transforms the model's internal parameters to understand the statistical relationships between original images and their expanded versions. This allows the model to generate expanded content that matches the original image's color distribution, texture, and style characteristics.
2Productivity
If existing image expansion methods are used, then expansion speed can be maintained, but style consistency between original image and expanded region deteriorates
Solution Approach 1:
The patent introduces an intermediary component - a pre-trained model - that serves as a bridge between the original image and the expanded regions. This model has been trained on a large dataset of image expansions and learns to generate content that is statistically consistent with the input image. The pre-trained model acts as an intermediary that translates the original image's style and characteristics into the expanded regions, ensuring style consistency while maintaining efficient generation speed through the use of established diffusion model techniques.
Data Source
AI summary
Embodiments of the present disclosure provide an image processing method, an apparatus, a device, a computer-readable storage medium, and a product. The method includes: obtaining an image to be processed and an image expansion text; performing a padding operation on the image to be processed based on a preset background to obtain a padded image; inputting the padded image and the image expansion text to a preset target model, the target model being obtained after a preset model to be trained is iteratively trained based on a preset training dataset, a training data pair including an original image, an image expansion description text, a random mask, a masked image, and a cropped image obtained through cropping based on the original image and the random mask; and performing an image expansion operation on the image to be processed based on a predicted noise that is output by the target model.