# Overcoming Adverse Conditions in Rescue Scenarios: A Deep Learning and Image Processing Approach
## 3 Results
### Details Recovery  
A crucial aspect of the pipeline is enhancing images through heightened detail, but the subjective nature of image quality poses a challenge in demonstrating improvements. To address this issue, we analysed several aspects of our results. Reference-based Image Quality Analysis: In this section, we evaluate the quality of the images produced by the pipeline by comparing them with reference images (R). R is obtained by using Automatic Exposure Bracketing (AEB) [35], a technique that captures the same scene several times at different exposure levels. To quantify the similarity between the reference images and the pipeline's outputs, we utilise two widely used image quality metrics: Mean Squared Error (MSE) and Structural Similarity Index (SSIM) [36]. Both metrics are computed for both the original and the output images with respect to the reference images. MSE computes the average of the squared differences between the pixel intensities of two images, while SSIM compares the structural information and texture of the images to provide a measure of similarity. By deriving both metrics for the original images and the pipeline's outputs, we determine whether the pipeline outputs are more similar to the reference images than the original ones. The average MSE and SSIM for both sets of images are presented in Table 1. In addition to reporting the average MSE and SSIM for the original and output images, we analysed the distribution of these metrics across all images used in our evaluation. In Appendix B, we use box plots to visualise the distribution of the MSE and SSIM values for both sets of images (Figure 11); we also display a sample of the images along with their corresponding MSE and SSIM values in Tables 12 and 13. Image Characteristics Evaluation: The image characteristics of both original images and pipeline results were assessed using a set of metrics, including Texture (T), Entropy (E), Object Count (OC), Segmentation Masks (SM), and Hue Similarity Index (HSI). T (Texture): Measures the visual pattern or structure of the images. We compute it by getting the variance of random images' windows. E (Shannon Entropy): Measures the degree of disorder in the images. A higher value indicates that the image has more information content, whereas a lower value indicates that the image has less degree of uncertainty. The comparison of Shannon entropy [37] of \(X\) and \(Y\) quantifies the changes in information content resulting from the correction process. OC (Object Count): OC represents the number of objects in an image. We obtain the metric by counting the connected components of the images. In particular, a connected component is defined as a set of pixels in the image that are connected through a path of neighbouring pixels of similar intensities. Table 2 displays the values of the three metrics mentioned above, calculated for both the original and corrected images. To reduce any noise that may have been introduced during the exposure correction process, we applied an average smoothing filter to the images. Taking this step ensures that the metrics reflect the amount of detail recovered from the corrected images. SM (Segmentation Masks): Segmentation masks are used to identify different regions or objects within an image. In particular, we used the mask \(M_{CE}\) to identify well-exposed areas of the images before and after correction, as shown in Figure 6. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **MSE** & **SSIM** \\ \hline Original (\(\chi\)) & 0.101 \(\pm\) 0.051 & 0.331 \(\pm\) 0.185 \\ Corrected (\(\Upsilon\)) & 0.033 \(\pm\) 0.014 & 0.476 \(\pm\) 0.130 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The table represents the average Mean Squared Error (MSE) and Structural Similarity Index (SSIM) for both the original images (\(\chi\)) and the corrected images (\(\Upsilon\)) with respect to their references (R).