Efficient and Robust Remote Sensing Image Denoising Using Randomized Approximation of Geodesics' Gramian on the Manifold Underlying the Patch Space
Journal:
arXiv
Published Date:
Apr 15, 2025
Abstract
Remote sensing images are widely utilized in many disciplines such as feature
recognition and scene semantic segmentation. However, due to environmental
factors and the issues of the imaging system, the image quality is often
degraded which may impair subsequent visual tasks. Even though denoising remote
sensing images plays an essential role before applications, the current
denoising algorithms fail to attain optimum performance since these images
possess complex features in the texture. Denoising frameworks based on
artificial neural networks have shown better performance; however, they require
exhaustive training with heterogeneous samples that extensively consume
resources like power, memory, computation, and latency. Thus, here we present a
computationally efficient and robust remote sensing image denoising method that
doesn't require additional training samples. This method partitions patches of
a remote-sensing image in which a low-rank manifold, representing the
noise-free version of the image, underlies the patch space. An efficient and
robust approach to revealing this manifold is a randomized approximation of the
singular value spectrum of the geodesics' Gramian matrix of the patch space.
The method asserts a unique emphasis on each color channel during denoising so
the three denoised channels are merged to produce the final image.
