## 5 Evaluation Strategies and Performance Metrics
We briefly present the nature of the five metrics mentioned above and their expressions, given in Table 8, for an image set for completeness. The image example was obtained from the Northeastern University (NEU) surface defect dataset, which includes six types of surface defects [121]. From the original image, a second version had semi-blurred compressed corruption (pixelisation), and the third version was corrupted by Gaussian distribution noise. Figure 10 shows the three image snapshots, and Figure 11 gives the absolute differences. Blurring and noise have shaded defects at different levels, causing a visible loss of quality. The absolute difference images also indicate the corruption effects, showing how the defects were hidden. After this point, these images will be handled as corrupted and noisy. Figure 12 shows the different images in a \(2\times 2\) array format. The bottom-right image is a fused image version of the noisy and corrupted image versions via alpha blending with the (fused) image intensities together as a single dataset. It can clearly be seen that the fused image had a purer texture than the sources in terms of noise and corrupting effects. Here, we will explain the evaluation metrics over these example images using the calculated values between the reference image and sources, including the fused image. _Root mean square error (RMSE):_ The RMSE, given the pixel intensity nature of the image, is a measure of the proximity of the two images that are compared. From a statistical viewpoint, the RMSE refers to the standard deviation of the error under investigation. A smaller value for the RMSE indicates closer similarity of the images (in the case of a fused image, the similarity to the original one) [123]. It is noted here that the MSE is closely related to the RMSE. Hence, we do not explain the MSE metric in detail. A useful example, in this case, can be seen in [90], which calculated the MSE and RMSE values for the radius and depth estimations of holes and stated that the bigger gaps between the estimated and expected (real) values might represent an insufficiency of the fusion operation. The authors attributed these high MSE and RMSE values to the blind zone effect of the ultrasonic  
Figure 11: Absolute difference imagesfor seeing how the corruption affected the images (with respect to original image). Images were converted to grayscale first. Figure 12: Montage of images with the fused version of the noisy and corrupted image versions (grayscale). imaging method for their case. In addition, the authors of [70] agreed that the RMSE is a measurement of detectability, showing that lower values indicated less contrast between the estimations and expectations. Table 9 shows the evaluation values for the example images, including corrupted, noisy and fused examples as a comparison. For our basic example application, it was not surprising to find less difference in the metrics between just the corrupted and original image, although the fused image had a considerably lower RMSE than the noisy image but slightly larger than the corrupted one. Even so, the lower RMSE value of the fused image indicates that the fusion successfully preserved the scratches' information. On the other hand, thresholding of these images indicated that preprocessing might affect the fusion success, as can be seen from the values of the same metrics. After a basic segmentation by adaptive thresholding, the fused image had the lowest RMSE value, visibly showing the highest similarity. Correlation coefficient (CC): This is an indicator that gives values in the range from \(-1\) to \(1\), revealing the relationship intensity between the sources and the equivalent fused image. The best match between the fused and original images has the highest correlation value [124]. In our example of fusion application, the correlations of the fused and pixelised images were quite close to each other (Table 9).