AI Image Coloring via Color A Priori Alignment
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Solution Overview
Problem
Existing image coloring methods using artificial intelligence often suffer from issues like color bleeding and fading, leading to reduced image processing accuracy and efficiency, especially when generating colored images from grayscale ones.
Innovation Solution
An image coloring method and apparatus based on artificial intelligence that acquires and transforms color a priori information to align with the image-to-be-colored, performs downsampling and modulation coloring processing, and upsampling to produce an accurately colored image, thereby improving processing accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional AI image coloring methods are used, then colored images can be generated from grayscale images, but color bleeding and color fading occur reducing image quality
Solution Approach 1:
The patent segments the coloring process into distinct stages: feature extraction from grayscale image, separate color prediction for different regions, and hierarchical fusion of color information. This segmentation allows precise control over color application in different image regions, preventing color bleeding while maintaining processing efficiency.
Solution Approach 2:
The patent applies local quality by using region-specific color prediction models that analyze and apply colors differently across various image regions. Each region receives color information tailored to its local characteristics, preventing unwanted color bleeding into adjacent regions while maintaining overall coloring efficiency.
2Extent of automation
If conventional AI coloring methods are applied, then processing can be automated, but color repair is needed increasing time consumption
Solution Approach 1:
The patent performs preliminary action by incorporating alignment constraints and color consistency checks during the initial coloring process. The model predicts colors with built-in constraints that prevent bleeding and fading from the outset, eliminating the need for subsequent manual color repair and maintaining full automation.
Solution Approach 2:
The patent implements feedback mechanisms where the coloring model continuously refines its predictions based on alignment with the original grayscale image structure and color consistency across regions. This feedback loop ensures high-quality output without requiring manual intervention for repairs.
3Manufacturing precision
If color a priori information is transformed to align with the image, then coloring accuracy improves, but processing complexity increases
Solution Approach 1:
The patent introduces an intermediary alignment transformation module that bridges the color a priori information and the target image space. This intermediary performs spatial alignment and feature matching, ensuring accurate color placement without requiring complex direct mapping, thus balancing accuracy with manageable processing complexity.
Data Source
AI summary
An image coloring method includes: acquiring first color a priori information about an target image; transforming the first color a priori information to obtain second color a priori information aligned with the target image; obtaining a first image feature based on the target image; performing modulation coloring processing on the first image feature based on the second color a priori information to obtain a second image feature; and obtaining a first colored image based on the second image feature and the second color a priori information, where the first colored image is aligned with the target image.