Optical Remote Sensing Image Denoising and Super-Resolution Reconstructing Using Optimized Generative Network in Wavelet Transform Domain

Abstract

High spatial quality (HQ) optical remote sensing images are very useful for target detection, target recognition and image classification. Due to the influence of imaging equipment accuracy and atmospheric environment, HQ images are difficult to acquire, while low spatial quality (LQ) remote sensing images are very easy to acquire. Hence, denoising and super-resolution (SR) reconstruction technology are the most important solutions to improve the quality of remote sensing images very effectively, which can lower the cost as much as possible. Most existing methods usually only employ denoising or SR technology to obtain HQ images. However, due to the complex structure and the large noise of remote sensing images, the quality of the remote sensing image obtained only by denoising method or SR method cannot meet the actual needs. To address these problems, a method of reconstructing HQ remote sensing images based on Generative Adversarial Network (GAN) named “Restoration Generative Adversarial Network with ResNet and DenseNet” (RRDGAN) is proposed, which can acquire better quality images by incorporating denoising and SR into a unified framework. The generative network is implemented by fusing Residual Neural Network (ResNet) and Dense Convolutional Network (DenseNet) in order to consider denoising and SR problems at the same time. Then, total variation (TV) regularization is used to furthermore enhance the edge details, and the idea of Relativistic GAN is explored to make the whole network converge better. Our RRDGAN is implemented in wavelet transform (WT) domain, since different frequency parts could be handled separately in the wavelet domain. The experimental results on three different remote sensing datasets shows the feasibility of our proposed method in acquiring remote sensing images.

Multi-stage image denoising with the wavelet transform

“…Specifically, each 4-layer wavelet transform and enhancement block as well as WEB includes three steps to fuse frequency features and structure information via combining signal processing techniques and discriminative learning for obtaining more useful information. The first step uses discrete wavelet transform (DWT) [9] to convert linear construct information to four frequency features. The second step utilizes structural network to guide signal processing techniques via a feature enhancement (FE) mechanism (also treated as a 4-layer residual dense block (RDB) in Fig.…”

A Novel Wavelet Selection Method for Seismic Signal Intelligent Processing

“…Álvarez-Cortés et al [6] proposed a nearlossless compression method for remote sensing data utilizing regression wavelet analysis (RWA). Feng et al [7] combined a wavelet with deep learning to reconstruct and denoise remote sensing images. In bioinformatic analysis, wavelets are widely used in fingerprint verification, biology for cell membrane recognition, protein analysis, electrocardiogram analysis, and so on.…”

URNet: A U-Shaped Residual Network for Lightweight Image Super-Resolution

“…In general, this problem is inherently ill-posed because many HR images can be downsampled to an identical LR image. To address this problem, numerous super-resolution (SR) methods are proposed, including early traditional methods [14][15][16][17] and recent learning-based methods [18][19][20]. Traditional methods include interpolation-based methods and regularization-based methods.…”