This paper presents a concept of noise similarity (NS), which can be used to refine the comparison of noisy patch and enhance the denoising power of the nonlocal means (NLM) filter. The fact behind this concept is that the similarity of noisy patch should depend on not only the underlying signal (noise free patches), but also the noise. Based on the concept of noise similarity, we derived a double NS (DNS) model, which converts the denoising problem into the problem of reducing two kinds of noise: one is the superimposed additive noise; the other is the deviation error, defined as another kind of noise denoting the difference between similar pixels on their true intensities. The former corresponds to noise suppression, while the latter corresponds to the restoration of image details. To evaluate the effectiveness of the DNS model, we proposed an iterative version of the NLM filter, where the two noise similarities can work collaboratively in the framework of maximum a posterior. Finally, the experimental results demonstrate that the proposed approach can provide competitive performance when compared with other state-of-the-art NLM filters.

This letter proposed a new nonlocal polarimetric synthetic aperture radar (PolSAR) filter based on discriminative similarity measure (DSM). The DSM, which is implemented in the form of maximum a posteriori (MAP), deals with the speckle and the underlying speckle-free signal of PolSAR data individually. Due to its MAP form, the DSM achieves good balance between the similarity of the speckle and the similarity of the underlying speckle-free signal. And then this balance can lead to excellent performance on both speckle smoothing and detail preservation. Results are shown to demonstrate its competence when compared with the state-of-the-art methods.

This paper presents a nonlocal Lee (NL-Lee) filter for polarimetric synthetic aperture radar despeckling. In the proposed NL-Lee filter, a kind of hybrid patch similarity measure is constructed by combining together the structure similarity introduced by the nonlocal means (NLM) filter and the homogeneity similarity introduced by the Lee filter, which works in a distributive way. This combination leads to two important advantages for the proposed nonlocal filter. One is the improved robustness to the NLM parameters such as patch size and search neighborhood size, since the patch regularity assumption can be enhanced by the introduced hybrid patch similarity; the other one is the better performance tradeoff between speckle removal and detail preservation, because of the good balance between structure similarity and homogeneity similarity obtained by the framework of the proposed NL-Lee filter. Experimental results are given to demonstrate its competitive denoising performance.

This paper presents a novel nonlocal Lee (NL-Lee) filter based on both structure similarity and homogeneity similarity, namely hybrid patch similarity, which can effectively enhance the patch regularity assumption. The proposed NL-Lee filter shares the framework of the NLM filter but combines the traditional Lee filter in a distributive way. Structure similarity from the NLM filter and homogeneity similarity from the Lee filter are well balanced in the new filter. As a result, the new NL-Lee filter obtains good trade-off between speckle smoothing and detail preservation and leads to state-of-the-art results.

In this paper, a novel method for water/land segmentation is proposed based on the framework of geodesic distance. The proposed method models the water/land according to the statistics of both the speckle and land covers, which leads to a fast point-wised coarse segmentation. Based on the water/land models, the boundary area between water and land can be localized with automatically generated class labels and adaptively determined bandwidth. Then the refined segmentation is implemented using an improved geodesic distance, combining the manifold idea to enlarge the inter-class differences. Experimental results on real synthetic aperture radar (SAR) images demonstrate the effectiveness and efficiency of the method. The bridges, harbors and coastline can be segmented correctly with very tiny details preserved.

Road extraction has become an important topic in remote sensing image processing. As a linear target, road has a simple geometric appearance and can be well represented by the Nonsubsampled Contourlet Transform (NSCT). This paper proposed a new road extraction method, which exploits the directional prior of the road in NSCT domain. Road prior includes not only the directional features, but also the context of intra-scale and inter-scale. The proposed method consists of three parts. The first two parts are context based including direction correction and direction enhancement; and the last part extracts road through non maximum suppression. Simulation results demonstrate that the proposed method shows an obvious improvement as compared with the method without the directional prior.

Bayesian nonlocal (NL) (BNL) means filter, as an extension of the NL means algorithm, provides a general framework for image denoising when dealing with different noise. However, this approach makes a strong assumption that image patch itself provides a good approximation on the true parameter, which leads to the bias problem particularly under serious speckle noise. Another disadvantage of the BNL filter is that the commonly used patch preselection method cannot effectively exclude the outliers. In this letter, a new form of the BNL filter is presented for the purpose of synthetic aperture radar image despeckling, which incorporates the technique of sigma filter to cope with the bias problem. In addition, pixel preselection is adopted based on the refined sigma range, which greatly contributes to the preservation of the image details such as edges, texture, and the strong reflective scatters. Experimental results illustrate that the proposed BNL filter reaches the state-of-the-art performance on both the visual quality and evaluation indexes.