Infrared Colorization via cGAN and Semantic Segmentation
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Solution Overview
Problem
Conventional techniques for colorizing infrared images often result in blurry or inaccurately colored RGB images due to averaging multiple valid color candidates, leading to a need for improved infrared colorization methods that enhance accuracy and detail.
Innovation Solution
The use of a conditional generative adversarial network (cGAN) to colorize IR images by transferring color statistics from an RGB image with an overlapping field of view, modifying encoded representations of synthesized RGB images, and employing segmentation and fine-tuning techniques to enhance color accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional end-to-end CNN techniques are used for infrared colorization, then the process can be automated, but the output images become blurry and lack detail due to averaging multiple valid color candidates
Solution Approach 1:
The patent segments the colorization process into distinct modules: a GAN-based colorization network for generating candidate colors, a segmentation network for identifying semantic regions, and a refinement module that applies region-specific color adjustments. This segmentation allows the system to handle different regions with different color characteristics rather than averaging everything uniformly, thereby maintaining sharpness and accuracy.
Solution Approach 2:
The patent applies local quality by analyzing semantic segments (e.g., sky, ground, objects) and applying different colorization strategies to each region. The refinement module adjusts colors based on local semantic information, ensuring that each region receives appropriate color treatment rather than a uniform approach, thus improving overall color accuracy and detail preservation.
2Device complexity
If conventional CNN techniques average multiple valid color candidates, then processing is simplified, but color accuracy deteriorates causing solid colored objects to appear with varied incorrect colors
Solution Approach 1:
Instead of averaging color candidates as conventional methods do, the patent inverts the approach by using a GAN to generate multiple plausible color variations and then using semantic segmentation to select the most appropriate color for each region. This inversion of the averaging process allows the system to maintain color accuracy while still simplifying the overall processing through automated GAN-based generation.
Solution Approach 2:
The patent introduces semantic segmentation as an intermediary between the GAN colorization and the final color output. This intermediary component analyzes the semantic content of each region and mediates the color selection process, ensuring that the most accurate color is chosen for each semantic region rather than using a simple average, thus improving color accuracy without significantly increasing processing complexity.
3Manufacturing precision
If segmentation and fine-tuning techniques are applied to enhance color accuracy, then color precision improves, but processing time and computational complexity increase
Solution Approach 1:
The patent applies preliminary action by pre-segmenting the image into semantic regions before the final color refinement step. This pre-processing segmentation allows the refinement module to work more efficiently by already having region boundaries defined, reducing the computational time needed for the fine-tuning process while maintaining high color precision across different semantic regions.
Data Source
AI summary
In various examples, infrared image data (e.g., frames of an infrared (IR) video feed) may be colorized by transferring color statistics from an RGB image with an overlapping field of view, by modifying one or more dimensions of an encoded representation of a generated RGB image, and/or otherwise. For example, segmentation may be applied to the IR and RGB image data, and the one or more colors or statistics may be transferred from a segmented region of the RGB image data to a corresponding segmented region of the IR image data. In some embodiments, synthesized RGB image data may be fined tuned by transferring color or color statistic(s) from corresponding real RGB image data, and/or by modifying one or more dimensions of an encoded representation of the synthesized RGB image data.