Super-Resolution Tile Stitching With Dynamic Halos for Seam Mitigation
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Solution Overview
Problem
Conventional super-resolution processing systems face issues with visible boundary artifacts or seams when images are tiled and stitched back together, due to the introduction of artificial edges from convolutional operations that omit neighboring pixel data at tile edges.
Innovation Solution
Dynamically control the size of input tiles and halo regions based on degradation thresholds associated with convolutional operations, using a super-resolution model to process images, and discard pixel data from the halo regions during stitching to minimize seam visibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If images are divided into tiles for super-resolution processing, then memory constraints are addressed and processing is made manageable, but visible boundary artifacts and seams are introduced between adjacent tiles
Solution Approach 1:
The image is divided into multiple tiles that are processed independently by the super-resolution model. Each tile is then stitched back together with adjacent tiles to form the final high-resolution output image. This segmentation allows the system to handle large images that would exceed memory constraints while maintaining processing manageability.
Solution Approach 2:
A halo region is added around each tile boundary before processing. This preliminary action ensures that when tiles are stitched together, the halo regions contain sufficient contextual information to minimize visible seams and boundary artifacts, allowing for smoother transitions between adjacent tiles.
2Quantity of substance
If convolutional operations are applied to tiles with purged adjacent pixel data, then memory consumption is reduced, but artificial edges are introduced that degrade output quality
Solution Approach 1:
The halo region is prepared in advance by copying pixel data from adjacent tiles before the convolutional operations are applied. This preliminary action ensures that when the model processes edge pixels, the halo region already contains the necessary contextual information, preventing artificial edges from being introduced while maintaining memory efficiency.
Solution Approach 2:
The halo region acts as an intermediary buffer between adjacent tiles. It stores pixel data that would otherwise be lost at tile boundaries, allowing the convolutional operations to access contextual information from adjacent tiles without requiring the entire image to be loaded into memory simultaneously.
3Measurement precision
If halo regions are enlarged to provide more context for convolutional operations, then receptive field coverage is improved, but processing time and computational resources increase
Solution Approach 1:
The size of the halo region is made dynamically adjustable rather than fixed. The system can adapt the halo region size based on the specific processing requirements, image characteristics, and available computational resources, allowing for optimization between receptive field coverage and processing time.
Solution Approach 2:
The halo region size is treated as a configurable parameter that can be changed to balance between computational cost and processing quality. By adjusting this parameter, the system can control the trade-off between having sufficient contextual information for accurate convolutional operations and limiting processing time and resource consumption.
Data Source
AI summary
Systems are provided for dynamically splitting input images into a plurality of input tiles for processing by a super-resolution model. The size and halo region of the input tiles is based on a degradation associated with the convolutional operations of the super-resolution model. The input tiles are processed by the super-resolution model to generate output tiles that are stitched together into output images that are of a higher or different resolution than the input images.