A remote sensing image enhancement method using mean filter and unsharp masking in non-subsampled contourlet transform domain

2016 ◽  
Vol 39 (2) ◽  
pp. 183-193 ◽  
Author(s):  
Lu Liu ◽  
Zhenhong Jia ◽  
Jie Yang ◽  
Nikola Kasabov
Keyword(s):  
Remote Sensing ◽  
Image Enhancement ◽  
High Frequency ◽  
Low Frequency ◽  
Remote Sensing Image ◽  
Contourlet Transform ◽  
Inverse Transformation ◽  
Unsharp Masking ◽  
Mean Filter ◽  
Enhancement Method

The intelligibility of an image can be influenced by the pseudo-Gibbs phenomenon, a small dynamic range, low-contrast, blurred edge and noise pollution that occurs in the process of image enhancement. A new remote sensing image enhancement method using mean filter and unsharp masking methods based on non-subsampled contourlet transform (NSCT) in the scope for greyscale images is proposed in this paper. First, the initial image is decomposed into the NSCT domain with a low-frequency sub-band and several high-frequency sub-bands. Secondly, linear transformation is adopted for the coefficients of the low-frequency sub-band. The mean filter is used for the coefficients of the first high-frequency sub-band. Then, all sub-bands were reconstructed into spatial domains using the inverse transformation of NSCT. Finally, unsharp masking was used to enhance the details of the reconstructed image. The experimental results show that the proposed method is superior to other methods in improving image definition, image contrast and enhancing image edges.

scholarly journals An Image Enhancement Method Based on Non-Subsampled Shearlet Transform and Directional Information Measurement

Information ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 308 ◽  
Author(s):  
Zhi Qu ◽  
Yaqiong Xing ◽  
Yafei Song
Keyword(s):  
Image Enhancement ◽  
High Frequency ◽  
Low Frequency ◽  
Objective Evaluation ◽  
Inverse Transformation ◽  
Information Measurement ◽  
Directional Information ◽  
Shearlet Transform ◽  
Enhancement Method ◽  
Blurred Images

Based on the advantages of a non-subsampled shearlet transform (NSST) in image processing and the characteristics of remote sensing imagery, NSST was applied to enhance blurred images. In the NSST transform domain, directional information measurement can highlight textural features of an image edge and reduce image noise. Therefore, NSST was applied to the detailed enhancement of high-frequency sub-band coefficients. Based on the characteristics of a low-frequency image, the retinex method was used to enhance low-frequency images. Then, an NSST inverse transformation was performed on the enhanced low- and high-frequency coefficients to obtain an enhanced image. Computer simulation experiments showed that when compared with a traditional image enhancement strategy, the method proposed in this paper can enrich the details of the image and enhance the visual effect of the image. Compared with other algorithms listed in this paper, the brightness, contrast, edge strength, and information entropy of the enhanced image by this method are improved. In addition, in the experiment of noisy images, various objective evaluation indices show that the method in this paper enhances the image with the least noise information, which further indicates that the method can suppress noise while improving the image quality, and has a certain level of effectiveness and practicability.

scholarly journals Denoising Method for Passive Photon Counting Images Based on Block-Matching 3D Filter and Non-Subsampled Contourlet Transform

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2462
Author(s):  
Xuan Wang ◽  
Liju Yin ◽  
Mingliang Gao ◽  
Zhenzhou Wang ◽  
Jin Shen ◽  
...  
Keyword(s):  
High Frequency ◽  
Photon Counting ◽  
Low Frequency ◽  
Operation Time ◽  
Objective Evaluation ◽  
Block Matching ◽  
Contourlet Transform ◽  
Denoising Method ◽  
Low Contrast ◽  
Enhancement Method

Multi-pixel photon counting detectors can produce images in low-light environments based on passive photon counting technology. However, the resulting images suffer from problems such as low contrast, low brightness, and some unknown noise distribution. To achieve a better visual effect, this paper describes a denoising and enhancement method based on a block-matching 3D filter and a non-subsampled contourlet transform (NSCT). First, the NSCT was applied to the original image and histogram-equalized image to obtain the sub-band low- and high-frequency coefficients. Regional energy and scale correlation rules were used to determine the respective coefficients. Adaptive single-scale retinex enhancement was applied to the low-frequency components to improve the image quality. The high-frequency sub-bands whose line features were best preserved were selected and processed using a symbol function and the Bayes-shrink threshold. After applying the inverse transform, the fused photon counting image was subjected to an improved block-matching 3D filter, significantly reducing the operation time. The final result from the proposed method was superior to those of comparative methods in terms of several objective evaluation indices and exhibited good visual effects and details from the objective impression.

Remote Sensing Image Enhancement Based on Wavelet Transformation

2012 ◽  
Vol 198-199 ◽  
pp. 223-226
Author(s):  
Ying Zhao ◽  
Ye Cai Guo
Keyword(s):  
Remote Sensing ◽  
Wavelet Transform ◽  
Theoretical Analysis ◽  
High Frequency ◽  
Wavelet Transformation ◽  
Low Frequency ◽  
Remote Sensing Image ◽  
Original Image ◽  
Remote Sensing Images ◽  
Enhancement Algorithm

The contrast of remote sensing images is very low, which include various noises. In order to make full used of remote sensing image information extraction and processing, the original image should have to be enhanced. In this paper the enhancement algorithm based on the biothogonal wavelet transform is proposed. Firstly, we have to eliminate the beforehand noise, and then take advantage of the non-linear wavelet transform to enhanced low-frequency and high- frequency coefficient respectively. Finally, the new picture is reconstruct by the transformed low-frequency and high-frequency coefficient. The efficiency of the proposed algorithm was proved by the theoretical analysis and computer simulations.

scholarly journals Saliency-Guided Nonsubsampled Shearlet Transform for Multisource Remote Sensing Image Fusion

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1756
Author(s):  
Liangliang Li ◽  
Hongbing Ma
Keyword(s):  
Remote Sensing ◽  
Image Fusion ◽  
High Frequency ◽  
Rapid Development ◽  
Low Frequency ◽  
Remote Sensing Image ◽  
Saliency Map ◽  
Shearlet Transform ◽  
Nonsubsampled Shearlet Transform ◽  
Remote Sensing Image Fusion

The rapid development of remote sensing and space technology provides multisource remote sensing image data for earth observation in the same area. Information provided by these images, however, is often complementary and cooperative, and multisource image fusion is still challenging. This paper proposes a novel multisource remote sensing image fusion algorithm. It integrates the contrast saliency map (CSM) and the sum-modified-Laplacian (SML) in the nonsubsampled shearlet transform (NSST) domain. The NSST is utilized to decompose the source images into low-frequency sub-bands and high-frequency sub-bands. Low-frequency sub-bands reflect the contrast and brightness of the source images, while high-frequency sub-bands reflect the texture and details of the source images. Using this information, the contrast saliency map and SML fusion rules are introduced into the corresponding sub-bands. Finally, the inverse NSST reconstructs the fusion image. Experimental results demonstrate that the proposed multisource remote image fusion technique performs well in terms of contrast enhancement and detail preservation.

Multi-spectral remote sensing image enhancement method based on PCA and IHS transformations

2011 ◽  
Vol 12 (6) ◽  
pp. 453-460 ◽  
Author(s):  
Shan-long Lu ◽  
Le-jun Zou ◽  
Xiao-hua Shen ◽  
Wen-yuan Wu ◽  
Wei Zhang
Keyword(s):  
Remote Sensing ◽  
Image Enhancement ◽  
Remote Sensing Image ◽  
Enhancement Method
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