Two-Stage Neural Network for Local Color Hints
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Solution Overview
Problem
Conventional image editing systems face challenges in accuracy, efficiency, and flexibility when colorizing and re-colorizing digital images, often resulting in inaccurate color predictions, color bleeding, and limited user control over the colorization process.
Innovation Solution
The implementation of a two-stage image colorization neural network system that utilizes a colorization neural network to generate colorized images from grayscale images and a re-colorization neural network to modify colors based on local hints provided by users, allowing for precise control over color changes and improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional image editing systems are used for colorization, then the process can be automated, but accuracy and user control are limited
Solution Approach 1:
The system implements feedback by allowing users to provide local hints about desired colors in specific regions, and the neural network iteratively adjusts color predictions based on this feedback while maintaining consistency with the overall image context
Solution Approach 2:
The system segments the colorization process into two stages: first generating an initial colorized image automatically, then allowing selective local modification based on user hints, thereby combining automation with precise control where needed
2Productivity
If conventional colorization methods are used, then processing can be performed, but color bleeding occurs and accuracy is reduced
Solution Approach 1:
The system applies different processing qualities to different regions: automated colorization with broader color priors for general areas, and localized precise color control in regions where users provide hints, preventing color bleeding at boundaries while maintaining processing efficiency
3Speed
If automatic colorization is applied to entire images, then processing speed is maintained, but user control over specific regions is lost
Solution Approach 1:
The system dynamically adapts the level of automation based on user input: initially processing the entire image automatically for speed, then enabling localized interactive control only in regions where users provide hints, maintaining both speed and flexibility
4Device complexity
If single-stage colorization networks are used, then device complexity is reduced, but adaptability to different colorization needs is limited
Solution Approach 1:
The system segments the colorization task into two distinct neural network stages: a first network for automatic initial colorization and a second network for local re-colorization based on user hints, providing versatility without excessive complexity
Data Source
AI summary
This disclosure describes methods, non-transitory computer readable storage media, and systems that utilize one or more stages of a two-stage image colorization neural network to colorize or re-colorize digital images. In one or more embodiments, the disclosed system generates a color digital image from a grayscale digital image by utilizing a colorization neural network. Additionally, the disclosed system receives one or more inputs indicating local hints comprising one or more color selections to apply to one or more objects of the color digital image. The disclosed system then utilizes a re-colorization neural network to generate a modified digital image from the color digital image by modifying one or more colors of the object(s) based on the luminance channel, color channels, and selected color(s).