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ISIS Documentation

hinoise

Perform NoiseFilter processing in the Pipeline Enviornment.

This application removes noise from HiRISE cubes. It also checks the pause points and removes any artifacts there. The application runs in a pipeline and starts off by getting the histogram of the image and gets the standard deviation, percentage of LIS pixels and MaxDN from the histogram stats. It then runs Cubenorm and processes the cubenorm stats given the Channel and Summing mode. If percentage of valid pixels in a column is less than CLEAR_FRACTION and if NULL_COLUMNS option is set then the stats for the columns Average, Median, StdDev, Minimum, Maximum will be set to 0 else it is set to 1. These changes are ouputted to new Stats files.

For images with summing equal to 1, depending on Channel 0/1, pause, width and direction is calculated. If the percentage of valid pixels in these columns are less than the user specified NONVALID_FRACTION then these columns are said to contain a high percentage of non-valid pixels and the stats for the columns stated above are set to 0.

These updated stats will form the input stats for cubenorm app which is run twice and output of which forms the input image one each for lowpass and highpass filters. The cubenorm basically clears the columns with stats equal to 0 which was previously manipulated. Perform highpass and lowpass filters for vertical destripping. The lowpass app is run with the options LPF_LINES, LPF_SAMPLES, LPF_MINPER. The highpass app is run with the options HPF_LINES, HPF_SAMPLES, HPF_MINPER. The output of these 2 filters are added.

Noisefilter app is run 3times with the options FLATTOL, TOLDEF, TOLMIN, TOLMAX, LOW, HIGH, REPLACE, SAMPLE, LINE, LISISNOISE and LRSISNOISE.

Now that the data is much cleaner another set of lowpass, highpass filters are performed on the image and added togather. This is the final product for non RED image.

Perform LPFZ filters if we have a RED filter image. For IR and BG filter data, assume that the HiColorNorm pipeline step will interpolate using the BG/RED and IR/RED ratio data. So another set of lowpass and highpass filters is performed on RED filter image and added to get the final product.

Categories

History

Sharmila Prasad2011-02-04 Original version converted from Eric Eliason's hical pipeline version 1.42
Sharmila Prasad2011-02-22 Renamed hinoise2 to hinoise. Previous redundant hinoise app has been removed.
Sharmila Prasad2011-03-02 Fix App Test - Enable parallel Test runs making input file name as part of unique temp files for pipeline.

Parameters

Files

Input cube to remove noise from
Type cube
File Mode input
Filter *.cub

Output cube with noise removed
Type cube
File Mode output
Pixel Type real

Remove Intermediate Files
Type boolean
Default false

Low Pass Filter Options

This is the total number of lines in the boxcar. It must be odd and can not exceed twice the number of lines in the cube.
Type integer
Default 251

This is the total number of samples in the boxcar. It must be odd and can not exceed twice the number of samples in the cube.
Type integer
Default 3

Minimum boxcar pixels required for filter. This option is the minimum number of valid pixels required in a boxcar for filtering to begin.
Type integer
Default 5

High Pass Filter Options

This is the total number of lines in the boxcar. It must be odd and can not exceed twice the number of lines in the cube.
Type integer
Default 251

This is the total number of samples in the boxcar. It must be odd and can not exceed twice the number of samples in the cube.
Type integer
Default 1

Minimum boxcar pixels required for filter. This option is the minimum number of valid pixels required in a boxcar for filtering to begin.
Type integer
Default 5

Noise Filter Options

This option indicates whether columns with valid pixels with ratio with Max Valid pixels less than CLEAR_FRACTION must be NULLed.
Type boolean
Default FALSE

If the difference between the input pixel and the boxcar average is greater then the tolerances given in TOLMIN or TOLMAX (values of DN), the pixel is considered to be noise and will be replaced. When a pixel is being checked for noise, a difference between the pixel and boxcar average is computed. If this difference is negative then TOLMIN will be used to determine if we have noise, hence dark noise will be removed by modifying TOLMIN.
Type double
Default 3.5

If you are attempting to remove white speckle or other bright noise then you should be modifying this parameter. When a pixel is being checked for noise, a difference between the pixel and boxcar average is computed. If this difference is positive then TOLMAX will be used to determine if we have noise, hence white noise will be removed by modifying TOLMAX.
Type double
Default 3.5

Minimum tolerance value used in the STDDEV filter. If the difference between the original pixel, and the average value of the noise filter is less than the FLATTOL then the output pixel will remain unchanged from the input pixel. Without this safeguard, very uniform inage areas (low standard deviation) will be excessively smoothed, creating areas of constant DN.
Type double
Default 1

Valid minimum pixel value that will be used in boxcar computation. If a pixel value is less than LOW then it will not be used when computing boxcar statistics.
Type double
Default 0

Use this value for noisefilter's TOLMIN parameter when doing hard noise filtering
Type double
Default 3.5

Use this value for noisefilter's TOLMAX parameter when doing hard noise filtering
Type double
Default 3.5

Type integer
Default 5

Type integer
Default 5

This is the total number of samples in the boxcar. It must be odd and can not exceed twice the number of samples in the cube. In general, the size of the boxcar does not cause the program to operate significantly slower. The shape of the filter can be used to control types of noise to remove. For example, a 3x3 or 5x5 boxcar can be used to remove speckle or salt and pepper noise. A 1 sample x 5 line boxcar with REPLACE=NULL could be used to remove dropped or noisy lines of data. Then use the lowpass program with a 3x3 boxcar to fill in the NULLed data.
Type integer
Default 7

This is the total number of lines in the boxcar. It must be odd and can not exceed twice the number of lines in the cube. In general, the size of the boxcar does not cause the program to operate significantly slower. The shape of the filter can be used to control types of noise to remove. For example, a 3x3 or 5x5 boxcar can be used to remove speckle or salt and pepper noise. A 1 sample x 5 line boxcar with REPLACE=NULL could be used to remove dropped or noisy lines of data. Then use the lowpass program with a 3x3 boxcar to fill in the NULLed data.
Type integer
Default 7

Fraction of valid pixels for which a column will not be NULL i.e any columns with valid points percentage less than this value will be set to NULL
Type double
Default 0

For each column of the cube a count is calculated of the number of valid pixels in the column. If any columns count divided by the maximum count is less than NONVALIDFRACTION then the pause points are nulled out. If the binning is anything other than one then all columns are checked with only the failing columns getting nulled out.
Type double
Default 0.90

Percent of LIS pixels to switch to hard noise filtering
Type double
Default 0.1

Fraction of high-end pixels to zap
Type double
Default 99.999

Fraction of high-end pixels to zap for hard noise filtering
Type double
Default 99.99