Anisotropic Weighted Total Variation Feature Fusion Network for Remote Sensing Image Denoising

Abstract

Remote sensing images are widely applied in instance segmentation and objetive recognition; however, they often suffer from noise, influencing the performance of subsequent applications. Previous image denoising works have only obtained restored images without preserving detailed texture. To address this issue, we proposed a novel model for remote sensing image denoising, called the anisotropic weighted total variation feature fusion network (AWTVF2Net), consisting of four novel modules (WTV-Net, SOSB, AuEncoder, and FB). AWTVF2Net combines traditional total variation with a deep neural network, improving the denoising ability of the proposed approach. Our proposed method is evaluated by PSNR and SSIM metrics on three benchmark datasets (NWPU, PatternNet, UCL), and the experimental results show that AWTVF2Net can obtain 0.12∼19.39 dB/0.0237∼0.5362 higher on PSNR/SSIM values in the Gaussian noise removal and mixed noise removal tasks than State-of-The-Art (SoTA) algorithms. Meanwhile, our model can preserve more detailed texture features. The SSEQ, BLIINDS-II, and BRISQUE values of AWTVF2Net on the three real-world datasets (AVRIS Indian Pines, ROSIS University of Pavia, HYDICE Urban) are 3.94∼12.92 higher, 8.33∼27.5 higher, and 2.2∼5.55 lower than those of the compared methods, respectively. The proposed framework can guide subsequent remote sensing image applications, regarding the pre-processing of input images.

High-Noise Grayscale Image Denoising Using an Improved Median Filter for the Adaptive Selection of a Threshold

“…Figures 3f, 4f and 5f present the denoising effects of the proposed improved TSETMF algorithm on the three images, respectively. It should be noted that the PDBTMF algorithm removes noise by comparing the numerical relationships between the current pixel and the neighboring pixels [64]. The MDBMF algorithm is a non-linear salt and pepper filter able to maintain the signal edges [65].…”

A Method of Image Restoration for Distortion of Object in Water-Air Cross-Media

“…For the cross-media distorted image restoration experiments, in order to evaluate the performance of the proposed algorithm in conventional scenarios, the algorithm of this paper is compared with the more advanced methods proposed by the existing AWTVFFNet [40], UnfairGAN [41], LPF [42], and CARNet [43]. The mean square error, peak signal-to-noise ratio, structural similarity, and time for image restoration of this paper's algorithm and the compared algorithms are shown in Figure 8.…”