Neural network nonuniformity correction method based on scene statistics
A non-uniformity correction and neural network technology, applied in radiation pyrometry, measuring devices, instruments, etc., can solve problems such as difficult to eliminate low-frequency spatial noise
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[0027] The following first analyzes the implementation process of the algorithm in principle:
[0028] A linear model is used to correct the infrared focal plane array, and the correction model is:
[0029] Y i,j (n)=G i,j (n) X i,j (n)+O i,j (n) (1)
[0030] where Y i,j Indicates the corrected output, X i,j Denotes the original image input, G i,j Indicates the correction gain, O i,j Indicates corrected image bias.
[0031] Taking expectation on both sides of the above formula can get an expression of the following form:
[0032] E[Y i,j ] = G i,j (n)·E[X i,j ]+O i,j (n) (2)
[0033] Where E[] is the expectation operator, E[Y i,j ] is the mean value of the corrected output, E[X i,j ] Input the mean value for the original image,
[0034] After subtracting formula (1) and formula (2), the following formula is obtained:
[0035] Y i,j (n)-E[Y i,j ] = G i,j (n)·(X i,j (n)-E[X i,j ]) (3)
[0036] Comparing formula (3) with formula (1), it can be seen that E[X ...
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