Bilateral Grid Prediction Network for Multi-Style Image Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current image quality enhancement (IqE) methods, such as the HDR-Net algorithm, are limited by their inability to support multiple styles and require extensive training for each style, making them inflexible and computationally costly, which hinders real-time image processing.
Innovation Solution
The method employs a bilateral grid prediction network that generates multiple bilateral grids from a single network, allowing for local and global adaptations of an input image, enabling flexible style application and reducing computational costs by using predefined style feature vectors and a feature decoder network, which can be trained end-to-end for multiple styles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single bilateral grid prediction network is used to generate multiple bilateral grids, then device complexity is reduced and training is simplified, but the ability to support multiple styles and perform local/global adaption is limited
Solution Approach 1:
The single bilateral grid prediction network is designed to perform multiple functions by generating different types of bilateral grids (local adaptation grids and global adaptation grids) that can handle various styles and regions. This multi-functional design allows one network to replace what would traditionally require multiple separate networks, reducing device complexity while maintaining versatility in supporting multiple styles through different grid types.
Solution Approach 2:
The image processing is segmented into different regions (first area and second area) using a mask, with different bilateral grids applied to different regions. This segmentation allows the single network to handle multiple styles by applying appropriate grids to appropriate regions, effectively achieving local and global adaption without requiring separate networks for each style.
2Manufacturing precision
If extensive training is performed for each style using separate networks, then style-specific accuracy is improved, but processing time increases and real-time performance is hindered
Solution Approach 1:
Multiple style-specific networks are merged into a single bilateral grid prediction network that can generate different bilateral grids for different styles. This merging eliminates the need to train and deploy multiple separate networks, significantly reducing processing time while maintaining style-specific accuracy through the use of style-appropriate bilateral grids generated by the unified network.
Solution Approach 2:
The system performs preliminary classification to identify the style and appropriate mask regions before applying the bilateral grid processing. This preliminary action allows the single network to quickly determine which bilateral grid to generate and apply, enabling real-time processing by avoiding the need for extensive trial-and-error training that would be required without such pre-processing.
3Adaptability or versatility
If multiple separate networks are used for different styles, then adaptability to various styles is improved, but computational costs and training complexity increase
Solution Approach 1:
The single bilateral grid prediction network is designed as a universal processor that can handle multiple styles through its ability to generate different types of bilateral grids. This universal design reduces computational costs by eliminating redundant network structures and training processes, while maintaining high adaptability through the flexible application of different grids to different styles and regions.
Data Source
Figure 1
Figure 2A
Figure 2B
AI summary
Image processing method, in particular Image Quality Enhancement, IqE, method, with providing an input image and a mask discriminating at least a first area and a second area of the input image; providing one bilateral grid prediction network receiving the input image and the mask and generating more than one bilateral grid; applying each of the bilateral grid to the first area and the second area of the input image in order to create the output image.