Shift Invariant Differential Image Interpolation for Pentile Matrix
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Solution Overview
Problem
Existing image processing methods struggle to accurately interpolate image data across edges and shapes that interfere with uniform illumination, leading to color shifts and blocking effects in images captured by mosaic structured color element arrays, particularly in non-shift invariant points within the Pentile Matrix pattern.
Innovation Solution
An image processing system that determines shift invariant points, calculates second-order derivatives at these points, and estimates derivatives for non-shift invariant points using neighboring values, allowing for interpolation of color data independent of location at maximum spatial sampling frequency, while also smoothing or sharpening the image data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If color interpolation is performed using existing methods in non-fully populated shift invariant matrix, then image data can be processed, but color shifts and blocking effects occur at edges and shapes
Solution Approach 1:
The patent segments the interpolation process into two distinct parts: shift-invariant points (where full 2x2 color information is available) and non-shift-invariant points (where color information is missing). By treating these segments differently and applying appropriate interpolation methods to each, the patent achieves accurate color reconstruction without the blocking effects that plague traditional uniform interpolation methods.
Solution Approach 2:
The patent applies different interpolation strategies based on the local characteristics of each pixel location. At shift-invariant points, direct calculation is used, while at non-shift-invariant points (particularly at edges and shapes), the patent uses gradient-based methods that adapt to local image structures. This local adaptation prevents the uniform application of interpolation methods that cause blocking artifacts.
2Manufacturing precision
If maximum spatial sampling frequency is used for interpolation, then image resolution is improved, but color values become location-dependent causing systematic color errors
Solution Approach 1:
The patent creates an equipotential surface for color values by using gradient information and second-order derivatives. By ensuring that the interpolated color values satisfy the same differential equations regardless of the starting location, the patent eliminates location-dependent systematic color errors while maintaining maximum spatial sampling frequency for high resolution.
Solution Approach 2:
The patent changes the mathematical parameters used for interpolation from simple linear methods to gradient-based methods involving first and second-order derivatives. This parameter change transforms the interpolation from a location-dependent process to one that produces consistent results across different locations, thereby stabilizing color values while preserving resolution.
3Device complexity
If simple interpolation methods are used, then processing complexity is reduced, but color accuracy at edges and shapes deteriorates
Solution Approach 1:
The patent introduces dynamic adaptation into the interpolation process by using gradient calculations and second-order derivatives that automatically adjust to local image features. At edges and shapes, the gradient-based methods dynamically adapt to the local structure, providing high color accuracy. In uniform regions, the method simplifies, maintaining reasonable processing complexity. This dynamic behavior resolves the contradiction between complexity and accuracy.
Data Source
AI summary
An image processing system interpolates image data of an image array by ascertaining shift invariant points and non-shift invariant points within the array. The average illumination and the second order derivative are determined for the shift invariant locations. The second order derivative and the intensity at the non-shift invariant locations for each of the non-shift invariant points are estimated. The color data for each color element is determined from the image data and second order derivative. The second order derivative is multiplied by a scaling factor for selectively smoothing and sharpening the second order derivative. The color data values of adjacent color element to enhance a resolution of the image data.