06-10-2012, 05:11 PM
Remote-Sensing Image Denoising Using Partial
Differential Equations and Auxiliary Images as Priors
Remote-Sensing Image Denoising.pdf (Size: 592.49 KB / Downloads: 29)
INTRODUCTION
IN RECENT years, several classes of denoising algorithms
such as total variation (TV) [1]–[3], wavelets [4]–[8], and
nonlocal means [9], [10] have all achieved much success. These
algorithms are based on different theories, and all show good
performance in denoising. When denoising an image, the TV
method makes use of the geometric features of the image,
the wavelet method makes use of the statistical features of
the coefficients, and the nonlocal means method makes use
of the redundancy in the image texture features. However, the
features that have been used by these methods all come from
the noisy image itself. In fact, the image features acquired by
other sensors from the same scene can also be used as priors in
denoising.
EXPERIMENTS AND RESULTS
To validate and compare the proposed method, we perform
the simulation experiments and real-data experiments on different
data sets. Multispectral and hyperspectral sensors acquire
multicomponent images. These data sets contain both the noisy
and noise-free images. A higher quality image can be obtained
from one of the sensors or from another part of the reflectance
spectrum with a higher SNR. We will apply such an image as
the noise-free image in the proposed method.
CONCLUSION
In this letter, the auxiliary noise-free image has been used
as a prior when we denoise one of the noisy images in the
multicomponent remote-sensing image. The edge information
of the reference image is fully considered, and a new smoothing
term reference to the edges is constructed in the proposed
method. Comprehensive experiments using different multispectral
and hyperspectral images with different levels of noise were
carried out. We have also compared the proposed method with
other state-of-the-art methods, and the better performance of
the proposed method is demonstrated. In particular, when the
variance of the noise in the multispectral image is large, the
advantage of the proposed method is more obvious.