Remote sensing and landsat image enhancement using multiobjective PSO based local detail enhancement

2018 ◽  
Vol 10 (9) ◽  
pp. 3563-3571 ◽  
Author(s):  
Rahul Malik ◽  
Renu Dhir ◽  
S. K. Mittal
Keyword(s):  
Remote Sensing ◽  
Image Enhancement ◽  
Landsat Image ◽  
Detail Enhancement ◽  
Local Detail

Visibility enhancement of color images using Type-II fuzzy membership function

2018 ◽  
Vol 32 (11) ◽  
pp. 1850130 ◽  
Author(s):  
Harmandeep Singh ◽  
Baljit Singh Khehra
Keyword(s):  
Image Enhancement ◽  
Color Images ◽  
Fuzzy Membership Function ◽  
Type Ii ◽  
Hidden Information ◽  
Color Gradients ◽  
Visibility Enhancement ◽  
Detail Enhancement ◽  
Local Detail ◽  
Atmospheric Light

Images taken in poor environmental conditions decrease the visibility and hidden information of digital images. Therefore, image enhancement techniques are necessary for improving the significant details of these images. An extensive review has shown that histogram-based enhancement techniques greatly suffer from over/under enhancement issues. Fuzzy-based enhancement techniques suffer from over/under saturated pixels problems. In this paper, a novel Type-II fuzzy-based image enhancement technique has been proposed for improving the visibility of images. The Type-II fuzzy logic can automatically extract the local atmospheric light and roughly eliminate the atmospheric veil in local detail enhancement. The proposed technique has been evaluated on 10 well-known weather degraded color images and is also compared with four well-known existing image enhancement techniques. The experimental results reveal that the proposed technique outperforms others regarding visible edge ratio, color gradients and number of saturated pixels.

scholarly journals Spatio-Temporal Super-Resolution Reconstruction of Remote-Sensing Images Based on Adaptive Multi-Scale Detail Enhancement

Sensors ◽  
2018 ◽  
Vol 18 (2) ◽  
pp. 498 ◽  
Author(s):  
Hong Zhu ◽  
Xinming Tang ◽  
Junfeng Xie ◽  
Weidong Song ◽  
Fan Mo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Super Resolution ◽  
Remote Sensing Images ◽  
Multi Scale ◽  
Spatio Temporal ◽  
Detail Enhancement

scholarly journals Application of high frequency filtration to remove cloudiness in Earth remote sensing images

2020 ◽  
Vol 223 ◽  
pp. 02010
Author(s):  
Valeriy Tutatchikov ◽  
Mikhail Noskov
Keyword(s):  
Remote Sensing ◽  
Image Quality ◽  
Image Enhancement ◽  
High Frequency ◽  
Digital Processing ◽  
Remote Sensing Images ◽  
Earth Remote Sensing ◽  
High Clouds

At present, methods of digital processing of Earth remote sensing images are widely used to improve the image quality. For example, many images are discarded due to high clouds in the images, which obscure objects of interest. In this paper, the possibility of using high- frequency global filters to reduce cloudiness in the image is considered, and the results of image enhancement are shown.

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.

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