Local area signal-to-noise ratio (LASNR) algorithm for image segmentation

Abstract

Many automated image-based applications have need of finding small spots in a variably noisy image. For humans, it is relatively easy to distinguish objects from local surroundings no matter what else may be in the image. We attempt to capture this distinguishing capability computationally by calculating a measurement that estimates the strength of signal within an object versus the noise in its local neighborhood. First, we hypothesize various sizes for the object and corresponding background areas. Then, we compute the Local Area Signal to Noise Ratio (LASNR) at every pixel in the image, resulting in a new image with LASNR values for each pixel. All pixels exceeding a pre-selected LASNR value become seed pixels, or initiation points, and are grown to include the full area extent of the object. Since growing the seed is a separate operation from finding the seed, each object can be any size and shape. Thus, the overall process is a 2-stage segmentation method that first finds object seeds and then grows them to find the full extent of the object.This algorithm was designed, optimized and is in daily use for the accurate and rapid inspection of optics from a large laser system (National Ignition Facility (NIF), Lawrence Livermore National Laboratory, Livermore, CA), which includes images with background noise, ghost reflections, different illumination and other sources of variation.

Signal and image processing research at the Lawrence Livermore National Laboratory

“…The imagery is next processed with the Local Area Signal-to-Noise Ratio (LASNR) image segmentation algorithm. 13 LASNR has been found to be highly effective at detecting imperfections in NIF optics inspection imagery. Essentially, it estimates the signal-to-noise ratio for each pixel in an image, using a local-area neighborhood surrounding the pixel to estimate noise statistics.…”

“…al, have developed a comprehensive suite of image analysis and pattern recognition tools to detect, characterize and track imperfections in NIF optics. [12][13][14] Imagery is preprocessed so that the background of the optic is dark, and imperfections in the optic appear bright. The imagery is next processed with the Local Area Signal-to-Noise Ratio (LASNR) image segmentation algorithm.…”

Inverse Coefficient of Variation Feature and Multilevel Fusion Technique for Hyperspectral and LiDAR Data Classification

“…In the matrix S, the values in the ith row contains the similarity between ith band and all bands. Since S has R 2 elements, which exponentially increases the dimensionality of features, we reduce the dimensions of the similarity matrix by employing the ICV concept, which is widely used in image processing [22]. ICV calculates the ratio of mean to standard deviation [23] as follows: and…”

Automatic classification of true and false laser-induced damage in large aperture optics

“…The signal image can be obtained by processing an FODI image using a Gaussian high-pass filter, whereas the noise image can be obtained by processing FODI images using a Gaussian low-pass filter. 10,11 According to the 12 feature parameters of the light intensity distribution on the CCD surface in the simulation formed by pit, Att 1 , and Att 2 , we present the 12 parameters that correspond to the former parameters for describing true and false damage in the experimental FODI images: (1) Table 3.…”