Pixel Outlier Detection Using Spatial-Color Distance Thresholds
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Solution Overview
Problem
Existing methods for detecting pixel outliers in digital images, such as hot and cold pixels, often result in high false positive rates, leading to the destruction of image structure and are not effectively validated on large image sets or real-world data from digital cameras.
Innovation Solution
A method that identifies singleton and paired outlier pixels in the RGB color space by calculating the Euclidean distance to nearest neighbors within a 3×3 patch, using a threshold fraction of the patch's diameter, and applying a sliding window approach to determine outlier status, while considering the color space transformations and spatial coordinates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rank ordering or vector median filter is used to replace noise pixels, then noise pixel detection performance is improved, but false positive rate increases and image structure is destroyed
Solution Approach 1:
The patent changes the detection parameter from color space distance alone to a combined parameter considering both color space distance and spatial distance. This dual-parameter approach allows better discrimination between actual outliers and normal variations, reducing false positives while maintaining detection accuracy.
Solution Approach 2:
The patent adds a spatial dimension to the detection process by considering the physical distance between pixels in addition to color space distance. This dimensional extension creates a more robust detection criterion that reduces false positives caused by color variations alone.
2Measurement precision
If rank ordering or vector median filter is used to replace noise pixels, then noise pixel detection performance is improved, but image structure is destroyed
Solution Approach 1:
By changing from single-parameter (color distance) to dual-parameter (color + spatial distance) detection, the patent achieves more selective outlier identification. This prevents replacement of legitimate image features that happen to have unusual colors, thereby preserving image structure while still detecting actual outliers.
Solution Approach 2:
The addition of spatial distance as a detection dimension creates a more selective filter that maintains image structure. Pixels must deviate in both color and spatial relationships to be flagged as outliers, preventing destruction of legitimate structural variations in the image.
3Measurement precision
If existing outlier detection methods are applied, then detection coverage is improved, but false positive rate increases
Solution Approach 1:
The patent extends detection from color space only to include spatial dimension, creating a two-dimensional detection framework. This allows comprehensive coverage of outliers while the spatial constraint prevents false positives from color variations alone, achieving both high coverage and low false positive rate.
Solution Approach 2:
The detection methodology changes from using color distance alone to using a composite parameter of color distance and spatial distance. This parameter transformation enables the system to maintain high detection coverage while filtering out false positives through the additional spatial criterion.
Data Source
AI summary
A method for identifying singleton outlier pixels in a selected color space in a digital image including a plurality of pixels, includes for each 3×3 patch of pixels in the image, calculating the diameter of the 3×3 patch of pixels. For each pixel in the patch, the distance to its nearest neighbor pixel within the patch is computed, as measured in the selected color space. The computed distance from each pixel in the patch is compared to its nearest neighbor with a threshold that is a preselected fraction of the diameter. A center pixel in the patch is identified as an outlier pixel if its calculated distance to its nearest neighbor is the largest distance to a nearest neighbor and exceeds the threshold.