Peppers denoising results with noise variance of 0.06.

Peppers denoising results with noise variance of 0.06.

<p>(a) Bilateral. (b) NLM. (c) Kuan. (d) Lee. (e) Lee-Enhanced. (f) Lee-Sigma. (g-i) 3D-NLE-BF with inputs of 2, 4, and 6 auxiliary images.</p>

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Bibliographic Details
Main Author: Haiyan Zhang (176919) (author)
Other Authors: Yang Liu (4829) (author), Guoyin Cai (9922178) (author)
Published: 2025
Subjects:
Space Science
Environmental Sciences not elsewhere classified
Biological Sciences not elsewhere classified
Mathematical Sciences not elsewhere classified
Information Systems not elsewhere classified
reducing image contrast
extensive imaging range
evaluated using enl
blurring edge contours
uniform filtering approach
multiplicative noise common
addressing additive noise
low spatial resolution
image denoising due
algorithm incorporates range
sar images vary
neighboring pixels based
denoised pixels contain
multitemporal sar data
weak noise pixels
neighboring pixel information
neighboring pixels
weak noise
sar images
multitemporal sar
filtering efficacy
extraneous data
denoised based
pixels along
noise levels
noise content
free pixels
filtered pixels
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widely employed
weighted average
useful information
target pixel
surface features
spatial stability
significant variations
selected bilateral
relying solely
pixel within
less effective
information loss
information expressed
higher similarity
grayscale distances
general applicability
considerable amount
comparison algorithms
bilateral filter
bf ).
algorithm proposed
algorithm capitalizes
algorithm begins
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Description
Summary:<p>(a) Bilateral. (b) NLM. (c) Kuan. (d) Lee. (e) Lee-Enhanced. (f) Lee-Sigma. (g-i) 3D-NLE-BF with inputs of 2, 4, and 6 auxiliary images.</p>

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