Hyperspectral Image Destriping via Pixel Variance Reduction
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Solution Overview
Problem
Conventional hyperspectral imaging systems, particularly those using Mercury-Cadmium-Telluride focal plane arrays, suffer from sensor calibration artifacts that result in false alarms and reduced target detection accuracy due to residual gain and offset errors, manifesting as columns or striping artifacts in detection images.
Innovation Solution
A method and system for processing hyperspectral images that involves selecting regions with striped columns, computing a global mean, and adjusting pixel values within these regions to align with the mean, thereby reducing variance and minimizing false alarms by normalizing pixel values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional HSI sensors are used for target detection, then imaging capability is provided, but sensor calibration artifacts create false alarms and reduce detection accuracy
Solution Approach 1:
The patent extracts and removes the harmful striping artifacts from the detection image by identifying abnormal pixels and replacing their values with averages from neighboring pixels, effectively separating the harmful artifacts from the useful target detection information
Solution Approach 2:
The patent converts the harmful striping artifacts into beneficial information by using the abnormal pixel identification and replacement process to enhance the overall image quality and reduce false alarms, turning the calibration errors into an opportunity for artifact reduction
2Manufacturing precision
If linear time invariant approximation is used to estimate gain and offset for destriping, then some artifact reduction is achieved, but nonlinear effects of clipping introduce skew into calculations reducing effectiveness
Solution Approach 1:
The patent changes the approach from linear time invariant approximation to a method that accounts for nonlinear effects by using abnormal pixel identification and replacement, adjusting the processing parameters to handle clipping effects properly
Solution Approach 2:
The patent uses neighboring pixels to create replacement values for abnormal pixels, copying information from adjacent valid pixels to restore the corrupted pixel values and reduce striping artifacts
3Manufacturing precision
If abnormal pixels are identified and replaced with average of neighboring pixels, then striping artifacts are reduced, but correct identification of abnormal pixels is required to avoid destriping errors
Solution Approach 1:
The patent implements a feedback mechanism where the detection filter output is analyzed to identify abnormal pixels, and the replacement process uses feedback from neighboring pixel values to iteratively reduce artifacts while maintaining image quality
Data Source
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AI summary
In accordance with various aspects of the disclosure, a system, a method, and computer readable medium having instructions for processing images is disclosed. For example, the method includes selecting, at an image processor, a region of a first image comprising a plurality of pixels. A mean value of pixels in the selected region is computed. From a plurality of sets of pixels in the region, a first subset of pixels in the region containing artifacts therein is selected. A value of each pixel in the first subset is compared with the mean value. The value of each pixel is adjusted based upon the comparing. The first image is reconstructed based upon the adjusted value of each pixel in the first subset, such that a variance of pixel values in the reconstructed image is lower than a variance of pixel values in the first image.