An Effective Image Denoising Method for UAV Images via Improved Generative Adversarial Networks
<p>An overview of the proposed DUGAN method. The networks include generative model G and discriminative model D.</p> "> Figure 2
<p>Testing results of several denoising methods for UAV images with several ground objects. In each group of images, the 1st image (<b>a</b><b><sub>1</sub></b>,<b>b</b><b><sub>1</sub></b>,<b>c</b><b><sub>1</sub></b>,<b>d</b><b><sub>1</sub></b>,<b>e</b><b><sub>1</sub></b>) is the ground truth; the 2nd image (<b>a<sub>2</sub></b>,<b>b<sub>2</sub></b>,<b>c<sub>2</sub></b>,<b>d<sub>2</sub></b>,<b>e<sub>2</sub></b>) is a noise image with noise level 35; and the 3rd image (<b>a<sub>3</sub></b>,<b>b<sub>3</sub></b>,<b>c<sub>3</sub></b>,<b>d<sub>3</sub></b>,<b>e<sub>3</sub></b>) presents the denoising results of method [<a href="#B8-sensors-18-01985" class="html-bibr">8</a>]. The 4th image (<b>a<sub>4</sub></b>,<b>b<sub>4</sub></b>,<b>c<sub>4</sub></b>,<b>d<sub>4</sub></b>,<b>e<sub>4</sub></b>) presents the denoising results of method [<a href="#B5-sensors-18-01985" class="html-bibr">5</a>]; the 5th image (<b>a<sub>5</sub></b>,<b>b<sub>5</sub></b>,<b>c<sub>5</sub></b>,<b>d<sub>5</sub></b>,<b>e<sub>5</sub></b>) presents the denoising results of method [<a href="#B17-sensors-18-01985" class="html-bibr">17</a>]; and the 6th image (<b>a<sub>6</sub></b>,<b>b<sub>6</sub></b>,<b>c<sub>6</sub></b>,<b>d<sub>6</sub></b>,<b>e<sub>6</sub></b>) presents the denoising results of the proposed method.</p> "> Figure 2 Cont.
<p>Testing results of several denoising methods for UAV images with several ground objects. In each group of images, the 1st image (<b>a</b><b><sub>1</sub></b>,<b>b</b><b><sub>1</sub></b>,<b>c</b><b><sub>1</sub></b>,<b>d</b><b><sub>1</sub></b>,<b>e</b><b><sub>1</sub></b>) is the ground truth; the 2nd image (<b>a<sub>2</sub></b>,<b>b<sub>2</sub></b>,<b>c<sub>2</sub></b>,<b>d<sub>2</sub></b>,<b>e<sub>2</sub></b>) is a noise image with noise level 35; and the 3rd image (<b>a<sub>3</sub></b>,<b>b<sub>3</sub></b>,<b>c<sub>3</sub></b>,<b>d<sub>3</sub></b>,<b>e<sub>3</sub></b>) presents the denoising results of method [<a href="#B8-sensors-18-01985" class="html-bibr">8</a>]. The 4th image (<b>a<sub>4</sub></b>,<b>b<sub>4</sub></b>,<b>c<sub>4</sub></b>,<b>d<sub>4</sub></b>,<b>e<sub>4</sub></b>) presents the denoising results of method [<a href="#B5-sensors-18-01985" class="html-bibr">5</a>]; the 5th image (<b>a<sub>5</sub></b>,<b>b<sub>5</sub></b>,<b>c<sub>5</sub></b>,<b>d<sub>5</sub></b>,<b>e<sub>5</sub></b>) presents the denoising results of method [<a href="#B17-sensors-18-01985" class="html-bibr">17</a>]; and the 6th image (<b>a<sub>6</sub></b>,<b>b<sub>6</sub></b>,<b>c<sub>6</sub></b>,<b>d<sub>6</sub></b>,<b>e<sub>6</sub></b>) presents the denoising results of the proposed method.</p> "> Figure 2 Cont.
<p>Testing results of several denoising methods for UAV images with several ground objects. In each group of images, the 1st image (<b>a</b><b><sub>1</sub></b>,<b>b</b><b><sub>1</sub></b>,<b>c</b><b><sub>1</sub></b>,<b>d</b><b><sub>1</sub></b>,<b>e</b><b><sub>1</sub></b>) is the ground truth; the 2nd image (<b>a<sub>2</sub></b>,<b>b<sub>2</sub></b>,<b>c<sub>2</sub></b>,<b>d<sub>2</sub></b>,<b>e<sub>2</sub></b>) is a noise image with noise level 35; and the 3rd image (<b>a<sub>3</sub></b>,<b>b<sub>3</sub></b>,<b>c<sub>3</sub></b>,<b>d<sub>3</sub></b>,<b>e<sub>3</sub></b>) presents the denoising results of method [<a href="#B8-sensors-18-01985" class="html-bibr">8</a>]. The 4th image (<b>a<sub>4</sub></b>,<b>b<sub>4</sub></b>,<b>c<sub>4</sub></b>,<b>d<sub>4</sub></b>,<b>e<sub>4</sub></b>) presents the denoising results of method [<a href="#B5-sensors-18-01985" class="html-bibr">5</a>]; the 5th image (<b>a<sub>5</sub></b>,<b>b<sub>5</sub></b>,<b>c<sub>5</sub></b>,<b>d<sub>5</sub></b>,<b>e<sub>5</sub></b>) presents the denoising results of method [<a href="#B17-sensors-18-01985" class="html-bibr">17</a>]; and the 6th image (<b>a<sub>6</sub></b>,<b>b<sub>6</sub></b>,<b>c<sub>6</sub></b>,<b>d<sub>6</sub></b>,<b>e<sub>6</sub></b>) presents the denoising results of the proposed method.</p> "> Figure 2 Cont.
<p>Testing results of several denoising methods for UAV images with several ground objects. In each group of images, the 1st image (<b>a</b><b><sub>1</sub></b>,<b>b</b><b><sub>1</sub></b>,<b>c</b><b><sub>1</sub></b>,<b>d</b><b><sub>1</sub></b>,<b>e</b><b><sub>1</sub></b>) is the ground truth; the 2nd image (<b>a<sub>2</sub></b>,<b>b<sub>2</sub></b>,<b>c<sub>2</sub></b>,<b>d<sub>2</sub></b>,<b>e<sub>2</sub></b>) is a noise image with noise level 35; and the 3rd image (<b>a<sub>3</sub></b>,<b>b<sub>3</sub></b>,<b>c<sub>3</sub></b>,<b>d<sub>3</sub></b>,<b>e<sub>3</sub></b>) presents the denoising results of method [<a href="#B8-sensors-18-01985" class="html-bibr">8</a>]. The 4th image (<b>a<sub>4</sub></b>,<b>b<sub>4</sub></b>,<b>c<sub>4</sub></b>,<b>d<sub>4</sub></b>,<b>e<sub>4</sub></b>) presents the denoising results of method [<a href="#B5-sensors-18-01985" class="html-bibr">5</a>]; the 5th image (<b>a<sub>5</sub></b>,<b>b<sub>5</sub></b>,<b>c<sub>5</sub></b>,<b>d<sub>5</sub></b>,<b>e<sub>5</sub></b>) presents the denoising results of method [<a href="#B17-sensors-18-01985" class="html-bibr">17</a>]; and the 6th image (<b>a<sub>6</sub></b>,<b>b<sub>6</sub></b>,<b>c<sub>6</sub></b>,<b>d<sub>6</sub></b>,<b>e<sub>6</sub></b>) presents the denoising results of the proposed method.</p> "> Figure 3
<p>Testing results of several denoising methods for UAV images with several ground objects. In each group of images, the 1st image (<b>a<sub>1</sub></b>,<b>b</b><b><sub>1</sub></b>,<b>c</b><b><sub>1</sub></b>,<b>d</b><b><sub>1</sub></b>,<b>e</b><b><sub>1</sub></b>)is the ground truth; the 2nd image (<b>a<sub>2</sub></b>,<b>b<sub>2</sub></b>,<b>c<sub>2</sub></b>,<b>d<sub>2</sub></b>,<b>e<sub>2</sub></b>) is a noisy image with noise level 55; and the 3rd image (<b>a<sub>3</sub></b>,<b>b<sub>3</sub></b>,<b>c<sub>3</sub></b>,<b>d<sub>3</sub></b>,<b>e<sub>3</sub></b>) presents the denoising results of method [<a href="#B8-sensors-18-01985" class="html-bibr">8</a>]. The 4th image (<b>a<sub>4</sub></b>,<b>b<sub>4</sub></b>,<b>c<sub>4</sub></b>,<b>d<sub>4</sub></b>,<b>e<sub>4</sub></b>) presents the denoising results of method [<a href="#B5-sensors-18-01985" class="html-bibr">5</a>]; the 5th image (<b>a<sub>5</sub></b>,<b>b<sub>5</sub></b>,<b>c<sub>5</sub></b>,<b>d<sub>5</sub></b>,<b>e<sub>5</sub></b>) presents the denoising results of method [<a href="#B17-sensors-18-01985" class="html-bibr">17</a>]; and the 6th image (<b>a<sub>6</sub></b>,<b>b<sub>6</sub></b>,<b>c<sub>6</sub></b>,<b>d<sub>6</sub></b>,<b>e<sub>6</sub></b>) presents the denoising results of the proposed method.</p> "> Figure 3 Cont.
<p>Testing results of several denoising methods for UAV images with several ground objects. In each group of images, the 1st image (<b>a<sub>1</sub></b>,<b>b</b><b><sub>1</sub></b>,<b>c</b><b><sub>1</sub></b>,<b>d</b><b><sub>1</sub></b>,<b>e</b><b><sub>1</sub></b>)is the ground truth; the 2nd image (<b>a<sub>2</sub></b>,<b>b<sub>2</sub></b>,<b>c<sub>2</sub></b>,<b>d<sub>2</sub></b>,<b>e<sub>2</sub></b>) is a noisy image with noise level 55; and the 3rd image (<b>a<sub>3</sub></b>,<b>b<sub>3</sub></b>,<b>c<sub>3</sub></b>,<b>d<sub>3</sub></b>,<b>e<sub>3</sub></b>) presents the denoising results of method [<a href="#B8-sensors-18-01985" class="html-bibr">8</a>]. The 4th image (<b>a<sub>4</sub></b>,<b>b<sub>4</sub></b>,<b>c<sub>4</sub></b>,<b>d<sub>4</sub></b>,<b>e<sub>4</sub></b>) presents the denoising results of method [<a href="#B5-sensors-18-01985" class="html-bibr">5</a>]; the 5th image (<b>a<sub>5</sub></b>,<b>b<sub>5</sub></b>,<b>c<sub>5</sub></b>,<b>d<sub>5</sub></b>,<b>e<sub>5</sub></b>) presents the denoising results of method [<a href="#B17-sensors-18-01985" class="html-bibr">17</a>]; and the 6th image (<b>a<sub>6</sub></b>,<b>b<sub>6</sub></b>,<b>c<sub>6</sub></b>,<b>d<sub>6</sub></b>,<b>e<sub>6</sub></b>) presents the denoising results of the proposed method.</p> "> Figure 3 Cont.
<p>Testing results of several denoising methods for UAV images with several ground objects. In each group of images, the 1st image (<b>a<sub>1</sub></b>,<b>b</b><b><sub>1</sub></b>,<b>c</b><b><sub>1</sub></b>,<b>d</b><b><sub>1</sub></b>,<b>e</b><b><sub>1</sub></b>)is the ground truth; the 2nd image (<b>a<sub>2</sub></b>,<b>b<sub>2</sub></b>,<b>c<sub>2</sub></b>,<b>d<sub>2</sub></b>,<b>e<sub>2</sub></b>) is a noisy image with noise level 55; and the 3rd image (<b>a<sub>3</sub></b>,<b>b<sub>3</sub></b>,<b>c<sub>3</sub></b>,<b>d<sub>3</sub></b>,<b>e<sub>3</sub></b>) presents the denoising results of method [<a href="#B8-sensors-18-01985" class="html-bibr">8</a>]. The 4th image (<b>a<sub>4</sub></b>,<b>b<sub>4</sub></b>,<b>c<sub>4</sub></b>,<b>d<sub>4</sub></b>,<b>e<sub>4</sub></b>) presents the denoising results of method [<a href="#B5-sensors-18-01985" class="html-bibr">5</a>]; the 5th image (<b>a<sub>5</sub></b>,<b>b<sub>5</sub></b>,<b>c<sub>5</sub></b>,<b>d<sub>5</sub></b>,<b>e<sub>5</sub></b>) presents the denoising results of method [<a href="#B17-sensors-18-01985" class="html-bibr">17</a>]; and the 6th image (<b>a<sub>6</sub></b>,<b>b<sub>6</sub></b>,<b>c<sub>6</sub></b>,<b>d<sub>6</sub></b>,<b>e<sub>6</sub></b>) presents the denoising results of the proposed method.</p> "> Figure 3 Cont.
<p>Testing results of several denoising methods for UAV images with several ground objects. In each group of images, the 1st image (<b>a<sub>1</sub></b>,<b>b</b><b><sub>1</sub></b>,<b>c</b><b><sub>1</sub></b>,<b>d</b><b><sub>1</sub></b>,<b>e</b><b><sub>1</sub></b>)is the ground truth; the 2nd image (<b>a<sub>2</sub></b>,<b>b<sub>2</sub></b>,<b>c<sub>2</sub></b>,<b>d<sub>2</sub></b>,<b>e<sub>2</sub></b>) is a noisy image with noise level 55; and the 3rd image (<b>a<sub>3</sub></b>,<b>b<sub>3</sub></b>,<b>c<sub>3</sub></b>,<b>d<sub>3</sub></b>,<b>e<sub>3</sub></b>) presents the denoising results of method [<a href="#B8-sensors-18-01985" class="html-bibr">8</a>]. The 4th image (<b>a<sub>4</sub></b>,<b>b<sub>4</sub></b>,<b>c<sub>4</sub></b>,<b>d<sub>4</sub></b>,<b>e<sub>4</sub></b>) presents the denoising results of method [<a href="#B5-sensors-18-01985" class="html-bibr">5</a>]; the 5th image (<b>a<sub>5</sub></b>,<b>b<sub>5</sub></b>,<b>c<sub>5</sub></b>,<b>d<sub>5</sub></b>,<b>e<sub>5</sub></b>) presents the denoising results of method [<a href="#B17-sensors-18-01985" class="html-bibr">17</a>]; and the 6th image (<b>a<sub>6</sub></b>,<b>b<sub>6</sub></b>,<b>c<sub>6</sub></b>,<b>d<sub>6</sub></b>,<b>e<sub>6</sub></b>) presents the denoising results of the proposed method.</p> "> Figure 4
<p>Due to the length concerns, we only show the matching results of 3 images (<b>b</b>,<b>c</b>,<b>e</b>). In each matching image, the left image is the original real clean UAV image, and the right image is the denoised UAV image (noise level 35). The 1st image (<b>b<sub>1</sub></b>,<b>c</b><b><sub>1</sub></b>,<b>e</b><b><sub>1</sub></b>) presents matching results from SIFT and the denoised UAV images using method [<a href="#B8-sensors-18-01985" class="html-bibr">8</a>] and original real clean UAV images. The 2nd image (<b>b</b><b><sub>2</sub></b>,<b>c<sub>2</sub></b>,<b>e<sub>2</sub></b>) presents matching results from SIFT and the denoised UAV images using method [<a href="#B5-sensors-18-01985" class="html-bibr">5</a>] and the original real clean UAV images. The 3rd image (<b>b</b><b><sub>3</sub></b>,<b>c<sub>3</sub></b>,<b>e<sub>3</sub></b>) presents matching results from SIFT and the denoised UAV images created by method [<a href="#B17-sensors-18-01985" class="html-bibr">17</a>] and original real clean UAV images; and the 4th image (<b>b</b><b><sub>4</sub></b>,<b>c<sub>4</sub></b>,<b>e<sub>4</sub></b>) presents matching results from SIFT and the denoised UAV images using proposed method and the original real clean UAV images.</p> "> Figure 4 Cont.
<p>Due to the length concerns, we only show the matching results of 3 images (<b>b</b>,<b>c</b>,<b>e</b>). In each matching image, the left image is the original real clean UAV image, and the right image is the denoised UAV image (noise level 35). The 1st image (<b>b<sub>1</sub></b>,<b>c</b><b><sub>1</sub></b>,<b>e</b><b><sub>1</sub></b>) presents matching results from SIFT and the denoised UAV images using method [<a href="#B8-sensors-18-01985" class="html-bibr">8</a>] and original real clean UAV images. The 2nd image (<b>b</b><b><sub>2</sub></b>,<b>c<sub>2</sub></b>,<b>e<sub>2</sub></b>) presents matching results from SIFT and the denoised UAV images using method [<a href="#B5-sensors-18-01985" class="html-bibr">5</a>] and the original real clean UAV images. The 3rd image (<b>b</b><b><sub>3</sub></b>,<b>c<sub>3</sub></b>,<b>e<sub>3</sub></b>) presents matching results from SIFT and the denoised UAV images created by method [<a href="#B17-sensors-18-01985" class="html-bibr">17</a>] and original real clean UAV images; and the 4th image (<b>b</b><b><sub>4</sub></b>,<b>c<sub>4</sub></b>,<b>e<sub>4</sub></b>) presents matching results from SIFT and the denoised UAV images using proposed method and the original real clean UAV images.</p> "> Figure 5
<p>In each matching image, the left image is the original real clean UAV image, and the right image is the denoised UAV image (noise level 55). The 1st image (<b>b<sub>1</sub></b>,<b>c</b><b><sub>1</sub></b>,<b>e</b><b><sub>1</sub></b>) presents matching results from SIFT and the denoised UAV images using method [<a href="#B8-sensors-18-01985" class="html-bibr">8</a>] and original real clean UAV images. The 2nd image (<b>b</b><b><sub>2</sub></b>,<b>c<sub>2</sub></b>,<b>e<sub>2</sub></b>) presents matching results from SIFT and the denoised UAV images using method [<a href="#B5-sensors-18-01985" class="html-bibr">5</a>] and the original real clean UAV images. The 3rd image (<b>b</b><b><sub>3</sub></b>,<b>c<sub>3</sub></b>,<b>e<sub>3</sub></b>) presents matching results from SIFT and the denoised UAV images created by method [<a href="#B17-sensors-18-01985" class="html-bibr">17</a>] and original real clean UAV images; and the 4th image (<b>b</b><b><sub>4</sub></b>,<b>c<sub>4</sub></b>,<b>e<sub>4</sub></b>) presents matching results from SIFT and the denoised UAV images using proposed method and the original real clean UAV images.</p> "> Figure 5 Cont.
<p>In each matching image, the left image is the original real clean UAV image, and the right image is the denoised UAV image (noise level 55). The 1st image (<b>b<sub>1</sub></b>,<b>c</b><b><sub>1</sub></b>,<b>e</b><b><sub>1</sub></b>) presents matching results from SIFT and the denoised UAV images using method [<a href="#B8-sensors-18-01985" class="html-bibr">8</a>] and original real clean UAV images. The 2nd image (<b>b</b><b><sub>2</sub></b>,<b>c<sub>2</sub></b>,<b>e<sub>2</sub></b>) presents matching results from SIFT and the denoised UAV images using method [<a href="#B5-sensors-18-01985" class="html-bibr">5</a>] and the original real clean UAV images. The 3rd image (<b>b</b><b><sub>3</sub></b>,<b>c<sub>3</sub></b>,<b>e<sub>3</sub></b>) presents matching results from SIFT and the denoised UAV images created by method [<a href="#B17-sensors-18-01985" class="html-bibr">17</a>] and original real clean UAV images; and the 4th image (<b>b</b><b><sub>4</sub></b>,<b>c<sub>4</sub></b>,<b>e<sub>4</sub></b>) presents matching results from SIFT and the denoised UAV images using proposed method and the original real clean UAV images.</p> "> Figure 5 Cont.
<p>In each matching image, the left image is the original real clean UAV image, and the right image is the denoised UAV image (noise level 55). The 1st image (<b>b<sub>1</sub></b>,<b>c</b><b><sub>1</sub></b>,<b>e</b><b><sub>1</sub></b>) presents matching results from SIFT and the denoised UAV images using method [<a href="#B8-sensors-18-01985" class="html-bibr">8</a>] and original real clean UAV images. The 2nd image (<b>b</b><b><sub>2</sub></b>,<b>c<sub>2</sub></b>,<b>e<sub>2</sub></b>) presents matching results from SIFT and the denoised UAV images using method [<a href="#B5-sensors-18-01985" class="html-bibr">5</a>] and the original real clean UAV images. The 3rd image (<b>b</b><b><sub>3</sub></b>,<b>c<sub>3</sub></b>,<b>e<sub>3</sub></b>) presents matching results from SIFT and the denoised UAV images created by method [<a href="#B17-sensors-18-01985" class="html-bibr">17</a>] and original real clean UAV images; and the 4th image (<b>b</b><b><sub>4</sub></b>,<b>c<sub>4</sub></b>,<b>e<sub>4</sub></b>) presents matching results from SIFT and the denoised UAV images using proposed method and the original real clean UAV images.</p> "> Figure 6
<p>(<b>a</b>) Represent the noisy images of cars and trucks with different noise levels; (<b>b</b>) Represent the denoising results of method [<a href="#B8-sensors-18-01985" class="html-bibr">8</a>]; (<b>c</b>) Represent the denoising results of method [<a href="#B5-sensors-18-01985" class="html-bibr">5</a>]; (<b>d</b>) Represent the denoising results of method [<a href="#B17-sensors-18-01985" class="html-bibr">17</a>]; (<b>e</b>) Represent the denoising results of proposed method.</p> ">
Abstract
:1. Introduction
2. Methods
2.1. Deep Architecture
2.1.1. Generative Model
2.1.2. Discriminative Model
2.2. Loss Function
3. Experiments
3.1. Experimental Setting
3.1.1. Experimental Data
3.1.2. Prameters Setting and Model Details
3.2. Comparison and Qualitative Evaluation
3.2.1. Comparion in the Traditional Way
3.2.2. Compare Denoising Results Using Image Matching
3.2.3. Compare Denoising Results Using Image Classification
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Images | Method [8] | Method [5] | Method [17] | Ours |
---|---|---|---|---|
Noise Level: 20 | ||||
a | 30.7543 | 30.9581 | 31.0174 | 31.2741 |
b | 30.3965 | 30.5583 | 30.6967 | 30.9458 |
c | 31.6907 | 31.9678 | 31.8534 | 32.1033 |
d | 29.6358 | 29.8145 | 29.9543 | 30.2104 |
e | 30.2235 | 30.4169 | 30.5576 | 30.7562 |
Average results 1 | 30.5942 | 30.7657 | 30.9856 | 31.1123 |
Images | Noise Level: 35 | |||
a | 28.8165 | 28.6967 | 28.8871 | 29.0564 |
b | 28.3084 | 28.5758 | 28.4312 | 28.9687 |
c | 29.4874 | 29.7321 | 29.7276 | 29.9576 |
d | 27.5172 | 27.6954 | 27.8958 | 28.1354 |
e | 28.2913 | 28.4782 | 28.6184 | 28.7858 |
Average results | 28.4358 | 28.6127 | 28.7968 | 28.9854 |
Images | Noise Level: 55 | |||
a | 26.1869 | 26.3112 | 26.4984 | 26.7156 |
b | 25.9276 | 26.1164 | 26.3658 | 26.5942 |
c | 27.1962 | 27.3335 | 27.4795 | 27.6614 |
d | 25.4956 | 25.6442 | 25.7841 | 25.9612 |
e | 26.0517 | 26.2654 | 26.3127 | 26.5154 |
Average results | 26.0014 | 26.2424 | 26.4912 | 26.6576 |
Images | Method [8] | Method [5] | Method [17] | Ours |
---|---|---|---|---|
Noise Level: 20 | ||||
a | 0.8799 | 0.8818 | 0.8841 | 0.8857 |
b | 0.8776 | 0.8797 | 0.8804 | 0.8823 |
c | 0.8857 | 0.8897 | 0.8895 | 0.8912 |
d | 0.8704 | 0.8723 | 0.8741 | 0.8765 |
e | 0.8731 | 0.8763 | 0.8779 | 0.8806 |
Average results | 0.8756 | 0.8781 | 0.8796 | 0.8821 |
Images | Noise Level: 35 | |||
a | 0.8345 | 0.8362 | 0.8379 | 0.8406 |
b | 0.8309 | 0.8339 | 0.8352 | 0.8371 |
c | 0.8398 | 0.8441 | 0.8433 | 0.8468 |
d | 0.8231 | 0.8249 | 0.8271 | 0.8289 |
e | 0.8276 | 0.8303 | 0.8319 | 0.8343 |
Average results | 0.8289 | 0.8312 | 0.8333 | 0.8369 |
Images | Noise Level: 55 | |||
a | 0.7682 | 0.7701 | 0.7719 | 0.7745 |
b | 0.7639 | 0.7661 | 0.7679 | 0.7701 |
c | 0.7739 | 0.7792 | 0.7798 | 0.7819 |
d | 0.7245 | 0.7271 | 0.7289 | 0.7315 |
e | 0.7589 | 0.7618 | 0.7641 | 0.7658 |
Average results | 0.7601 | 0.7647 | 0.7662 | 0.7689 |
Images | Method [8] | Method [5] | Method [17] | Ours |
---|---|---|---|---|
Noise Level 35/55 | Noise Level 35/55 | Noise Level 35/55 | Noise Level 35/55 | |
a | 110/53 | 114/74 | 127/78 | 150/88 |
b | 160/101 | 205/141 | 240/145 | 274/154 |
c | 96/66 | 104/70 | 118/76 | 135/78 |
d | 136/115 | 138/129 | 157/144 | 192/154 |
e | 177/154 | 192/162 | 211/179 | 260/215 |
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Wang, R.; Xiao, X.; Guo, B.; Qin, Q.; Chen, R. An Effective Image Denoising Method for UAV Images via Improved Generative Adversarial Networks. Sensors 2018, 18, 1985. https://doi.org/10.3390/s18071985
Wang R, Xiao X, Guo B, Qin Q, Chen R. An Effective Image Denoising Method for UAV Images via Improved Generative Adversarial Networks. Sensors. 2018; 18(7):1985. https://doi.org/10.3390/s18071985
Chicago/Turabian StyleWang, Ruihua, Xiongwu Xiao, Bingxuan Guo, Qianqing Qin, and Ruizhi Chen. 2018. "An Effective Image Denoising Method for UAV Images via Improved Generative Adversarial Networks" Sensors 18, no. 7: 1985. https://doi.org/10.3390/s18071985
APA StyleWang, R., Xiao, X., Guo, B., Qin, Q., & Chen, R. (2018). An Effective Image Denoising Method for UAV Images via Improved Generative Adversarial Networks. Sensors, 18(7), 1985. https://doi.org/10.3390/s18071985