# Rectification for Stitched Images with Deformable Meshes and Residual Networks
## 4 Results
### Qualitative Comparison of Image Rectification  
To visually demonstrate the effectiveness of RIS-DMRN in image rectification, we divided the test set into two parts--one with more global contextual information and the other with more local detailed information. The algorithm was tested on both sets, and the results were compared qualitatively (Figure 5). Specifically, the study showcases the effects of different input irregular images, image completion results, RIS-DMRN processed images, and ground truth label images in scenes where global correlations are more prominent, such as natural landscapes.  
It is evident that image completion can fully rectify the image into a rectangle (Figure 5), but it relies heavily on pixel-level adjustments based on context, making it overly dependent on surrounding information. This dependency may lead to the inaccurate filling of missing parts, resulting in generated images that appear unrealistic or unnatural, and may  
Figure 5: Rectification effect of different irregular images under images with more global correlation information. “Reference” in the figure represents the experimental result of image completion.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **BIQUE**\(\downarrow\) & **NIQE**\(\downarrow\) \\ \hline RPTIV [26] & 14.045 & 16.927 \\ DRIS [21] & 13.796 & 16.421 \\ Label & 11.017 & 14.763 \\ Ours & 13.562 & 16.027 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative comparison of non-referenced assessment indicators. Blind Image Quality Evaluator (BIQUE) and Natural Image Quality Evaluator (NIQE) are “no-reference” metrics designed to assess image quality without an original reference image. BIQUE estimates image quality by considering local contrast, structural information, and global color and brightness variations. NIQE focuses on natural images, assessing quality through the analysis of statistical features such as gradients, luminance, and color distribution. An upward arrow in the table indicates that the larger its value, the higher the image quality, and vice versa.  
even cause a certain degree of decrease in image clarity [36; 54]. In contrast, the rectangular images generated by RIS-DMRN closely resemble the real label images. RIS-DMRN performs well in maintaining the global rigidity or curvature of target objects in the image, attempting to preserve the original appearance without introducing local barrel or pincushion distortions.  
In scenarios with dense local information, we compare the results of image completion and RIS-DMRN processing when dealing with irregular images (Figure 6). It is observed that image completion methods often lead to deformations in local rigid structures during the rectification process. Moreover, when addressing the boundaries of missing regions, noticeable boundary effects are common, as image completion methods need to ensure smooth transitions between the filled area and the surrounding region, leading to prominent boundary artifacts [55; 56; 57]. In addition, this article also presents other qualitative comparison results, as shown in Appendix A Figures A1 and A2.  
RIS-DMRN, with its finer mesh design, can control the loss and deformation of local information within a certain range. This ensures that local shape changes do not excessively impact the deformation of adjacent meshes, guaranteeing the preservation of local information during the rectification process. Additionally, constraints in different mesh directions in RIS-DMRN allow it to adapt to various deformations of rigid and curved structures, enabling a better fit to the original shape of the image during the deformation process.