# Remote Sensing Image Enhancement Based on Non-Local Means Filter in NSCT Domain
## 1 Introduction  
Due to the limitations of image acquisition devices and external factors, the resolution of remote sensing images is not very satisfactory, so image enhancement techniques play a very important role in image analysis [1; 2]. The purpose of image enhancement is to highlight the useful features of the image [3; 4], and image enhancement methods are widely used in areas such as atmospheric sciences [5], medical image processing [6; 7], satellite image analysis [8; 9], and remote sensing [10].  
So far, image enhancement approaches can be divided into two categories: spatial domain and frequency domain. In terms of spatial domain enhancement, Histogram equalization (HE) is a widely used method to enhance images, but HE may result in the over-enhancement phenomenon of image processing, and make the image too dark in some regions [1; 11]. In order to make up for the shortcomings of the HE method, some improved algorithms based on HE have been proposed, such as contrast limited adaptive histogram equalization (CLAHE) [12], brightness preserving bi-histogram equalization (BBHE) [13], dualistic sub-image histogram equalization (DSIHE) [14], exposure based sub-image histogram equalization (ESIHE) [15], etc. Gamma correction [16] is also a useful method to improve the contrast of digital images, and some improved techniques have been proposed based on this, such as adaptive gamma correction with weighting distribution (AGCWD) [17] and adaptive gamma correction for image enhancement [1]; these approaches have achieved good performance in image enhancement. With the research of image enhancement algorithms, image enhancement methods based on frequency domain transform came into being, with the most typical approach being wavelet transform [18]. Due to the good time-frequency localization and multi-resolution of wavelets, the wavelet has been widely used in texture analysis, image processing, and other fields. Arising from wavelet theory, some new frequency domain enhancement techniques have been proposed,such as curvelet transform [19] and contourlet transform [20]. However, the contourlet transform does not have translation invariance. Due to the existence of this shortcoming, the pseudo-Gibbs phenomenon can easily appear around the singular points in the image restoration. In order to deal with this weakness, the nonsubsampled contourlet transform (NST) was proposed [21; 22]. The NST transform has many merits such as time-frequency locality, multi-resolution, multi-direction, and anisotropy, which can sparsely represent the image. The NST has since been widely applied in image fusion and image enhancement [23; 24]. In this paper, a novel remote sensing enhancement approach based on a non-local means filter in NST domain is proposed.  
The reminder of this paper is expressed as follows: Section 2 introduces the theoretical methods adopted in this article, including the nonsubsampled contourlet transform and non-local means filter models; the implementation of the proposed approach is described in Section 3; the experimental results and discussions are offered in Section 4; and the conclusions of the proposed remote sensing image enhancement method are described in Section 5.