Camera ISP Parameter Tuning for Noise-Texture Balance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image signal processing methods lack an optimal combination of parameter values that can satisfy all scenarios, leading to trade-offs between image quality aspects such as noise, texture, and brightness, necessitating a need for a method to tune parameters effectively.
Innovation Solution
A method involving capturing raw images, generating multiple rendered images with different parameter sets, calculating image quality scores, and determining optimal parameter values through comparison, interpolation, or extrapolation based on these scores, using optimization algorithms and deep learning networks to achieve balanced image quality across various conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If higher gain (ISO) is used to capture images in lower lighting conditions, then brightness is improved, but noise increases
Solution Approach 1:
The patent applies parameter changes by adjusting denoising algorithm parameters based on the ISO gain value. When higher ISO is used, the system automatically modifies denoising parameters to compensate for the increased noise, thereby resolving the contradiction between brightness improvement and noise generation.
2Object-generated harmful factors
If denoising algorithm parameters are tuned to reduce noise, then noise is reduced, but texture is lost
Solution Approach 1:
The patent applies local quality by using different denoising parameters for different regions of the image. The system identifies texture regions and applies milder denoising to preserve texture details while applying stronger denoising to uniform regions, thus resolving the contradiction between noise reduction and texture preservation.
3Reliability
If parameter values are tuned to optimize one aspect of image quality, then that aspect is improved, but other aspects deteriorate
Solution Approach 1:
The patent applies dynamics by making the parameter tuning process adaptive rather than static. The system dynamically adjusts multiple parameters based on the specific image characteristics and processing requirements, allowing the parameter set to adapt to different scenarios while maintaining overall image quality across multiple aspects.
Data Source
AI summary
A method of tuning parameters for image signal processing is provided. The method includes capturing at least one raw image. The method further includes generating a first rendered image by rendering the raw image based on a first parameter value set, and generating a second rendered image by rendering the raw image based on a second parameter value set. The method further includes calculating a first image quality score set for the first rendered image, and calculating a second image quality score set for the second rendered image. The method further includes generating a third parameter value set based on the first parameter value set, the second parameter value set, the first image quality score set, and the second image quality score set.