Abstract
Stripe noise removal continues to be an active field of research for remote image processing. Most existing approaches are prone to generating artifacts in extreme areas and removing the stripe-like details. In this paper, a weighted double sparsity unidirectional variation (WDSUV) model is constructed to reduce this phenomenon. The WDSUV takes advantage of both the spatial domain and the gradient domain’s sparse property of stripe noise, and processes the heavy stripe area, extreme area and regular noise corrupted areas using different strategies. The proposed model consists of two variation terms and two sparsity terms that can well exploit the intrinsic properties of stripe noise. Then, the alternating direction method of multipliers (ADMM) optimal solver is employed to solve the optimization model in an alternating minimization scheme. Compared with the state-of-the-art approaches, the experimental results on both the synthetic and real remote sensing data demonstrate that the proposed model has a better destriping performance in terms of the preservation of small details, stripe noise estimation and in the mean time for artifacts’ reduction.
Highlights
The remote sensing image plays an important role in environment monitoring, resource monitoring and military and battlefield situation observations [1,2,3]
A series of experimental results are presented to verify the destriping property of the proposed algorithm on stripe noise removal, small details reservation and artifacts’ reduction. Both synthesized images and real noise corrupted remote sensing images were tested, and we compared the proposed model with several typical state-of-the-art destriping methods, including the spatial domain filter method based on guided filter (GF-based) [40], the frequency domain filter method wavelet-Fourier filtering method (WAFT) [15], the unidirectional variational based models, including the unidirectional variation model (UV) method [9], hybrid unidirectional total variation (HUTV) method [19], sparse UV model (SUV) [24] and convolutional neural network based method stripe noise removal convolutional neural network (SNRCNN) [31]
In the real stripe noise image experiments, we selected the mean of inverse coefficient of variation (MICV) and mean of mean relative deviation (MMRD) [24] indices to validate the effect of the destriping approaches
Summary
The remote sensing image plays an important role in environment monitoring, resource monitoring and military and battlefield situation observations [1,2,3]. The output of sensing images often suffers from stripe-like noise, which seriously degrades the image’s visual quality and yields a negative influence on high-level application, such as target detection and data classification [4,5,6,7]. Due to the inconsistent responses of detectors and the imperfect calibration of amplifiers, the gain and offset of true signals are various, producing stripe noise on Moderate Resolution Imaging spectrometer (MODIS) data and hyperspectral images. Three typical striped images are displayed, and the stripe effect is obvious by zooming in. This noise is periodic for 10 pixels for the detectors’ calibration errors and the charge-coupled device array scanning forward and reverse across-track [8,9]
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