Local Convolutional Neural Network for Mobile Image Processing

The patent replaces traditional mechanical image processing methods with neural network-based processing. The neural network model, trained on server-side computational resources, is deployed to mobile devices to perform complex image modifications such as face manipulation, style transfer, and object detection. This substitution enables mobile devices to execute sophisticated image processing tasks that would otherwise be computationally infeasible on such hardware, directly improving image modification capability while managing power consumption through efficient model design.

The patent employs parameter optimization techniques to adjust the neural network model for mobile deployment. By modifying model parameters such as reducing layer depth, decreasing filter counts, and optimizing activation functions, the system achieves a balance between processing capability and power consumption. These parameter changes enable the neural network to perform complex image modifications on mobile devices with limited computational resources and battery capacity.

The patent applies preliminary action by pre-training the neural network model on extensive image datasets using powerful server-side computational resources before deploying it to mobile devices. The model is pre-processed and optimized during the training phase, allowing it to perform complex image modifications efficiently on mobile hardware without requiring real-time training. This preliminary preparation enables fast inference speed on resource-constrained devices.

The patent replaces traditional mechanical image processing algorithms with a neural network-based system that has been optimized for mobile deployment. By substituting conventional processing methods with a pre-trained neural network, the system achieves faster processing times for complex tasks such as face manipulation and style transfer, as the neural network can leverage learned patterns rather than computing from scratch.

The patent extracts the essential image processing functionality from complex server-side systems and encapsulates it in a standalone neural network model that can run independently on mobile devices. By taking out the core processing capability and packaging it as a portable model, the system reduces network dependency and simplifies the overall architecture while maintaining high processing capability. The mobile device becomes self-sufficient for image modifications without requiring constant network communication.

The patent creates a universal neural network model that can perform multiple image processing functions on mobile devices, including face detection, manipulation, style transfer, and object detection. This multi-functional model replaces the need for multiple specialized processing systems, reducing device complexity and eliminating network dependency while maintaining comprehensive processing capability across various image modification tasks.