# Infrared Stripe Correction Algorithm Based on Wavelet Analysis and Gradient Equalization
## 5 Experiment and Analysis
### Infrared Image Test Evaluation
#### 5.3.2 Stripe Correction Algorithm Comparison Evaluation  
Three sets of image sequences with different scenes and noises are used for the specific implementation. Corrected results for raw infrared image are shown in Figures 12-14. Observing the image, it can be found that the processing effects of the four correction algorithms are obviously different. The correction results based on TV algorithm can protect the details and edge information  
Figure 11: Raw image Gaussian filtering Guided filtering Our method. Top: raw image and strip removal results using smooth filtering and our proposed method. Bottom: Zoom-in views of the highlighted region. Note that visually insignificant but important targets (e.g., trees and leaves) are smoothed out in the output of normal denoising algorithms. But in our results can still be easily identified. The input image is available under the CC-BY license [9]. Please zoom in to see details in the pictures. of the image to the maximum extent, but many obvious vertical strips are still visible in the image (as shown in the red ellipse of the figure). The results obtained by MHE algorithm can better remove vertical strips but will over-smooth important details in infrared images. The results obtained by CNN algorithm have a good visual effect, but for the weak texture image, details will be lost (as shown in Figure 13). For our own method not only removes the stripe noise without introducing the ghost image, but also saves most of the edge details of the image. Figure 12: Stripe non-uniformity correction results for suitcase. (**a**) Raw; (**b**) TV; (**c**) CNN; (**d**) MHE; (**e**) Ours. Please zoom in to see details in the pictures. Figure 13: Stripe non-uniformity correction results for leaves. (**a**) Raw; (**b**) TV; (**c**) CNN; (**d**) MHE; (**e**) Ours. Please zoom in to see details in the pictures. To further prove the above point, we calculated the column mean of the raw image and the algorithm-corrected image. The result is shown in Figure 15. The effect of stripe noise can be regarded as the rapid change in the column mean. Figure 15a shows the change curve of the column mean value in Figure 14a. These changes are reduced after correction by the TV algorithm (Figure 15b), but some small fluctuations can still be observed, indicating residual non-uniformity without correction. MHE algorithm (Figure 15c) basically corrects these small fluctuations. However, the changes of image details are also lost, owing to being overly smooth. The proposed algorithm (Figure 15e) and CNN algorithm (Figure 15d) have similar results; small changes corresponding to image details are preserved, with stripe noise being simultaneously smoothed. In addition to the visual contrast, we applied two objective indicators to evaluate the algorithm, image roughness index \(\rho\) and average vertical gradient error (AVGE) [30]. The image roughness \(\rho\) represents the richness of the image details. Its mathematical formula is as follows:  
\[\rho(f)=\frac{\|h*f\|+\|h^{T}*f\|}{\|f\|} \tag{19}\]  
where \(h\) represents a differential filter; symbol \(*\) represents a convolution operation; \(\|\cdots\|\) represents a first-order norm. The lower the value of \(\rho\), namely having low nonuniformity. Taking the raw infrared image and the simulated image (artificially add different intensity noise) as the samples to be processed through the TV, MHE, CNN and our proposed algorithm to obtain \(\rho\) values in different scenarios. It can be found from Tables 4 and 5 that the value of \(\rho\) obtained by our proposed algorithm is the lowest, indicating that compared with the other image destriping algorithms, our proposed algorithm has an excellent ability to destripe noise. Figure 14: Stripe non-uniformity correction results for people. (**a**) Raw; (**b**) TV; (**c**) CNN; (**d**) MHE; (**e**) Ours. Please zoom in to see details in the pictures. Figure 15: Column mean transformation curves of original and corrected images.