Photosite Matrix Value Estimation for RGBZ Sensor Defects
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Solution Overview
Problem
Photographic sensors with defective photosites generate aberrant values, leading to undesirable artifacts in images, and the integration of depth sensors in RGBZ sensors results in a loss of colorimetric information, necessitating a method to estimate missing values effectively.
Innovation Solution
A method that defines a kernel zone around the missing or incorrect value, determines weights for neighboring values based on colorimetric component differences, and interpolates these values to estimate the missing value, considering texture continuity and colorimetric proximity, which can be executed by dedicated or programmable processing units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If depth sensors replace photosites in RGBZ sensors, then depth information is captured, but colorimetric information is lost at depth sensor locations
Solution Approach 1:
The patent uses neighboring photosite values as intermediaries to estimate the missing colorimetric values at depth sensor locations. By treating adjacent photosite measurements as proxy data, the method reconstructs the color information that would have been captured by photosites, allowing the fused image to maintain colorimetric continuity without requiring actual photosites at every location.
2Productivity
If simple interpolation methods are used to estimate missing values, then processing is fast, but artifacts are introduced in the reconstructed image
Solution Approach 1:
The patent dynamically changes the interpolation parameters (weights, kernel size, and method type) based on local image characteristics such as texture complexity and edge presence. In homogeneous regions, simpler and faster methods are used, while in complex regions with edges or textures, more sophisticated interpolation with higher computational cost is applied. This adaptive parameter adjustment maintains image quality while optimizing processing speed.
3Measurement precision
If a large kernel zone is used for estimation, then estimation accuracy improves, but processing complexity increases
Solution Approach 1:
The patent implements dynamic kernel adaptation where the kernel size and shape are adjusted based on local image features. In regions with sufficient neighboring data and simple textures, a smaller kernel is used to reduce computation. In regions with complex textures or limited neighboring data, the kernel is expanded to include more pixels for better estimation. This dynamic adjustment optimizes the balance between accuracy and computational complexity.
Data Source
AI summary
An embodiment method for estimating a missing or incorrect value in a table of values generated by a photosite matrix comprises a definition of a zone of the table comprising the value to be estimated and other values, referred to as neighboring values, and an estimation of the value to be estimated based on the primary neighboring values and the weight associated with these primary neighboring values, wherein a weight of each neighboring value, referred to as primary neighboring value, of the same colorimetric component as that of the missing or incorrect value to be estimated, is determined according to differences between neighboring values disposed on an axis and neighboring values disposed parallel with this axis and positioned in relation to this axis on the same side as the primary neighboring value for which the weight is determined.