Neural Network Image Stylization System
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Solution Overview
Problem
Existing image processing technologies struggle to apply stylization effects to entire images realistically, often resulting in low quality due to resizing and computational inefficiencies, particularly when transforming large images.
Innovation Solution
A neural network-based stylization system that includes a generative model trained on a target domain dataset, capable of applying stylization effects to both main objects and backgrounds, using paired source and target image datasets to enhance realism and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If image resizing is applied to improve processing efficiency, then computational resources are reduced, but image quality and stylization effect quality deteriorate
Solution Approach 1:
The image processing is divided into two distinct stages: first, a neural network generates a stylization effect on a cropped region of the main object; second, this localized effect is generalized and applied to the entire image. This segmentation allows high-quality processing of the main object while efficiently extending the effect to the background, resolving the contradiction between processing efficiency and effect quality.
Solution Approach 2:
The patent transitions from processing only the two-dimensional cropped region to applying the stylization effect across the entire image canvas. By using the cropped region as a template and generalizing its stylistic properties to the full image, the system achieves comprehensive stylization without processing the entire high-resolution image directly, thus maintaining quality while improving efficiency.
2Reliability
If neural network processes entire large images, then comprehensive stylization is achieved, but computational resources and processing time increase significantly
Solution Approach 1:
The system first identifies and crops the main object region before applying the neural network for stylization. This preliminary action focuses computational resources on the most important area (the main object) while a smaller, more manageable image size, reducing overall processing time. The stylization effect is then generalized to the entire image, achieving comprehensive coverage without the computational burden of processing the full high-resolution image from scratch.
3Productivity
If stylization is applied only to main object, then processing speed is maintained, but background realism deteriorates
Solution Approach 1:
The neural network is trained and configured to perform multiple functions: it first processes the cropped main object region to generate stylization effects, then generalizes these effects to apply to the entire image including the background. This multi-functionality allows the system to maintain processing speed while achieving realistic stylization across all regions of the image, not just the main object.
Data Source
AI summary
Systems herein describe a stylization system that accesses an input image, generates a paired image dataset using a first neural network, generates a stylized target image based on the input image by applying the stylization effect on an entire portion of the input image using a second neural network trained on the paired image dataset, and causes display of the stylized target image on a graphical user interface of a computing device.