Conditional GAN Image Repair via Object Segmentation
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Solution Overview
Problem
Existing image completion methods using deep learning require massive training data and parameters, often resulting in repaired objects that are unnatural and inefficient in terms of operation, failing to meet user needs effectively.
Innovation Solution
A method involving the configuration of conditional generative adversarial networks (cGANs) specific to object types, utilizing lightweight deep belief networks for image repair, which includes corruption feature training and a judging model to generate and evaluate repaired images, reducing the number of parameters and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If massive training data and non-classified deep learning models are used for image repair, then the model has commonality and can handle various images, but the repaired objects have defects or appear unnatural and the method requires massive parameters and operations
Solution Approach 1:
The patent segments the general image repair model into multiple specialized sub-models, each trained on specific object types (e.g., face repair model, vehicle repair model, animal repair model). This segmentation allows each sub-model to focus on specific object characteristics, improving repair quality and naturalness while reducing the need for massive parameters in each specialized model.
Solution Approach 2:
The patent applies local quality by creating specialized repair models for different object types rather than using a single general model. Each local model (face, vehicle, animal) is optimized for its specific object category, ensuring that the repair process respects the unique characteristics and requirements of each object type, thereby improving naturalness and reducing defects.
2Adaptability or versatility
If massive training data and parameters are used in deep learning models, then the model can handle diverse images, but the operational complexity and computational requirements increase significantly
Solution Approach 1:
The patent divides the large-scale general repair model into multiple smaller specialized models. Each specialized model requires fewer parameters and less computational resources while maintaining high adaptability for its specific object type. This segmentation reduces operational complexity and makes the system more efficient.
Solution Approach 2:
The patent changes the parameter configuration by training each specialized model with fewer parameters tailored to specific object types rather than using a large number of general parameters. This parameter optimization reduces computational requirements and operational complexity while maintaining or improving repair quality for each object category.
3Ease of manufacture
If a single general repair model is used, then the system is simple to implement, but the repaired images lack naturalness and realism
Solution Approach 1:
The patent segments the repair system into multiple specialized models for different object types. While this increases the number of models, each model remains relatively simple to implement and train. The segmentation approach improves realism by ensuring that each object type is repaired by a model specialized in that category, capturing unique characteristics that a general model would miss.
Solution Approach 2:
The patent creates a universal framework that handles multiple object types through specialized sub-models. The overall system maintains ease of implementation through a standardized architecture, while the specialized models provide the necessary realism for different object categories. This multi-functional approach balances simplicity and realism.
Data Source
AI summary
A method of image completion comprises: constructing the image repair model and constructing a plurality of conditional generative adversarial networks according to a plurality of object types; inputting the training image corresponding to the plurality of objective types such that the plurality of conditional generative adversarial networks respectively conduct a corruption feature training; inputting the image in need of repair and respectively conducting an image repair through the plurality of conditional generative adversarial networks to generate a plurality of repaired images; and judging a reasonable probability of the plurality of repaired images through a probability analyzer, choosing an accomplished image and outputting the accomplished image through an output interface.