High-Resolution Patch Fusion for Early-Stage ISP Detail Recovery
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Solution Overview
Problem
Existing image signal processing (ISP) systems face inaccuracies due to limited receptive fields, leading to inconsistent processing and loss of fine image details during downscaling, which affects semantic segmentation and temporal noise reduction.
Innovation Solution
A patch management system that collects high-resolution patches during downscaling and analyzes them using a Deep Neural Network (DNN) to enhance texture reproduction and temporal noise reduction, while minimizing computational overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full resolution image is used for image signal processing, then processing accuracy and texture reproduction are improved, but computational complexity and processing time increase
Solution Approach 1:
The image is divided into multiple patches that are processed independently. Each patch is analyzed for texture characteristics and processed according to its specific properties, allowing parallel processing that reduces overall computational complexity while maintaining full-resolution processing accuracy.
Solution Approach 2:
Instead of applying complex processing to the entire full-resolution image, the system applies processing selectively to specific regions or patches based on their texture characteristics. This partial action approach maintains accuracy where needed while reducing overall computational burden.
2Device complexity
If downscaling is applied during early-stage analysis, then computational complexity is reduced, but fine image details and texture information are lost
Solution Approach 1:
The system segments the full-resolution image into multiple patches and processes each patch individually. This allows the system to work with smaller data units (reducing computational complexity) while preserving the original full-resolution texture information in each patch.
Solution Approach 2:
The system creates multiple copies of the full-resolution image data in the form of overlapping or non-overlapping patches. These patches are then processed independently, allowing the system to work with replicated data units that preserve fine details while enabling more efficient processing.
Data Source
AI summary
Systems and methods for a patch management system that combines the benefits of working on a low-resolution image with added cues from the high-resolution image. The patch management system collects high-resolution patches during the downscaling process. The high-resolution patches are analyzed using a Deep Neural Network to detect fine details that are lost in the downscaled image. By fusing the high-resolution patch-level information with semantic segmentation results, the ISP blocks are provided with both global context and local details, improving texture reproduction and temporal noise reduction while adding minimal overhead compared to standard downscaled processing. The patch management system can also be used for tasks such as optical flow calculation from a downscaled image. By strategically selecting image areas for high-resolution patches, the system minimizes computational overhead as compared to processing a full-resolution image. The patch management system offers a cost-effective solution for devices that have limited processing power.