Neural Network Image Colorizing Model Segmentation
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Solution Overview
Problem
Older black and white images lack the visual appeal of color images, and existing methods fail to effectively convert them into realistic color images due to technical limitations.
Innovation Solution
An image colorizing method that classifies grayscale images into human face or human images, using corresponding neural network-based models to generate color information, and synthesizes the images into LAB and RGB color models for accurate colorization, employing deep learning technology and convolutional generative adversarial networks for training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single unified image colorizing model is used, then the device complexity is reduced, but the colorization accuracy and realism deteriorate
Solution Approach 1:
The patent divides the image colorizing model into multiple specialized sub-models, each trained on specific types of images (e.g., human faces, landscapes, objects). This segmentation allows each sub-model to specialize in particular image characteristics, thereby improving colorization accuracy and realism for different image types while managing overall system complexity through modular architecture.
2Manufacturing precision
If multiple type-specific image colorizing models are used, then the colorization accuracy is improved, but the device complexity increases
Solution Approach 1:
The patent implements a preliminary image classification step that categorizes input images into different types before routing them to appropriate specialized colorizing models. This preliminary action enables the system to select the most suitable model for each image type, improving colorization accuracy while managing complexity through intelligent model selection rather than maintaining all models simultaneously active.
3Manufacturing precision
If deep learning technology is employed for training, then the colorization realism is improved, but the training time and computational resources increase
Solution Approach 1:
The patent segments the training process by creating separate training pipelines for different image types using specialized sub-models. Each sub-model is trained on curated datasets specific to its image category, which reduces the computational burden compared to training a single universal model on all image types. This segmentation enables more efficient resource utilization and faster training convergence for each specialized model.
Data Source
AI summary
An image colorizing method and device are provided, which relate to the field of image processing technology. The method includes acquiring a grayscale image to be colorized, classifying the grayscale image to determine the grayscale image is a human face image or a human image, providing the grayscale image to a neural network based image colorizing model corresponding to the type of the grayscale image to obtain color information for respective pixels in the grayscale image. The image colorizing model is a human face image colorizing model if the grayscale image is a human face image, the image colorizing model is a human image colorizing model if the grayscale image is a human image. The method further includes synthesizing the grayscale image and the color information to obtain a color image.