Image Processing Method for Interference Removal and Restoration
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Solution Overview
Problem
Existing image processing methods fail to accurately restore original images after removing reticulated patterns or watermarks, leading to reduced image recognition efficiency and increased difficulty in recognition tasks.
Innovation Solution
An image processing method that involves removing interference factors from original images to obtain sample images, segmenting them into sub-images, determining target sub-images with similar attributes using clustering algorithms, and combining these sub-images with different attributes to form a target image, thereby restoring the original image with high precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reticulated patterns and watermarks are added to images for protection, then image security and copyright protection are improved, but image recognition efficiency deteriorates and recognition difficulty increases
Solution Approach 1:
The patent segments the image into multiple local regions and processes each region independently to remove reticulated patterns and watermarks. By dividing the image into manageable segments and applying targeted processing to each, the method effectively removes interference patterns while preserving the overall image quality and maintaining high recognition efficiency.
Solution Approach 2:
The patent extracts and removes interference factors (reticulated patterns and watermarks) from the original image through specialized processing algorithms. By identifying and eliminating these harmful elements while retaining the essential image content, the method achieves both copyright protection removal and high-quality image restoration for recognition purposes.
2Object-generated harmful factors
If existing image processing methods are used to remove interference factors, then interference patterns are removed, but image restoration accuracy deteriorates
Solution Approach 1:
The patent divides the image into multiple local regions and processes each region separately with region-specific algorithms. This segmentation approach allows for more precise restoration of each area while effectively removing interference patterns, thereby improving overall image restoration accuracy compared to global processing methods.
Solution Approach 2:
The patent applies different processing strategies to different regions of the image based on local characteristics. By adapting the processing method to each specific region's features and interference patterns, the method achieves higher restoration accuracy while effectively removing harmful factors from each area.
3Object-generated harmful factors
If multiple processing methods are applied to remove interference factors, then interference pattern removal effectiveness is improved, but processing complexity increases
Solution Approach 1:
The patent segments the image into multiple regions and applies appropriate processing methods to each segment. This segmentation strategy enables the use of multiple processing techniques to improve interference removal effectiveness while managing complexity by organizing the process into structured, region-based operations rather than applying all methods globally.
Solution Approach 2:
The patent introduces intermediate processing steps and algorithms that bridge the original image and the final restored image. These intermediary processing methods systematically remove interference factors through multiple stages, improving removal effectiveness while maintaining organized and manageable processing complexity.
Data Source
AI summary
A first image to be processed is identified, where the first image includes one or more interference factors. The one or more interference factors are removed from the first image using a plurality of different interference factor removal techniques to obtain a plurality of sample images, where each of the plurality of sample images is associated with a particular interference factor removal technique. Each sample image of the plurality of sample images is segmented into a plurality of sample sub-images based on a segmentation rule, where each sample sub-image is associated with an attribute. A plurality of target sub-images is determined from the plurality of sample su b-images, where each target sub-image comprises a combination of sample sub-images associated with a common attribute, and where each target sub-image is associated with a different attribute. The plurality of target sub-images associated with different attributes is combined into a target image.