# Estimating the Spectral Response of Eight-Band MSFA One-Shot Cameras Using Deep Learning
## 5 Discussion  
The performance of the adapted VDSR, DRCN, and LapSRN models reveals significant insights into their capabilities to estimate the spectral responses of 8-band one-shot MSFA cameras. Each model has distinct characteristics that directly influence their effectiveness, and the metric results provide valuable insight into their overall performance. The DRCN model is the best of the three in quantitative and qualitative assessment. With the lowest values for loss, RMSE (root mean square error), and MAE (mean absolute error), as well as the highest values for PSNR (peak signal-to-noise ratio) and the SSIM  
Figure 16: Presentation of the Character image, from the ground truth to the spectral response of the adapted VDSR, DCRN, and LapSRN models. Figure 17: Presentation of the Tshirts2 image, from the ground truth to the spectral response of the adapted VDSR, DCRN, and LapSRN models. (structural similarity index), DRCN demonstrates an exceptional ability to minimize the error of spectral response estimation. These results show that DRCN does not just reduce the numerical differences between predictions and actual values and generate images perceived to be of better quality than the original images. DRCN's complex architecture enables deep learning of image features, which is the key to superior performance. The depth of the network, combined with advanced optimization techniques, allows DRCN to capture finer details and improve spectral fidelity, which is crucial for applications where color and texture accuracy is paramount. Although not as efficient as DRCN, the LapSRN model shows interesting results in terms of error minimization (loss, RMSE, MAE) and produces slightly higher-quality images in terms of PSNR compared to VDSR. LapSRN is designed to minimize visual artifacts while maintaining reasonable accuracy, making it suitable for applications where visual quality is more important than structural or spectral fidelity. VDSR, for its part, stands out for its ability to preserve structures and maintain high spectral precision, as shown by SSIM and SAM (spectral angle mapper) scores. Although its error minimization performance is not as impressive as that of DRCN or LapSRN, VDSR excels in faithfully reproducing details and fine features of images, which is crucial in scenarios where structural fidelity has priority. Overall, DRCN emerges as the best-performing model for spectral response estimation among those evaluated, providing superior image quality and better spectral fidelity. However, selecting the appropriate model should not be based on overall performance alone; it must also consider the application's specific requirements, including available resources, use conditions, and priorities regarding visual quality, structural fidelity, or spectral precision. DRCN is ideal for applications requiring high accuracy and optimal image quality. At the same time, VDSR and LapSRN could be favored in contexts where other aspects, such as speed or simplicity of the model, are more important. We use data covering wavelengths from 420 nm to 720 nm with an interval of 10 nm. This setting presents a first limitation: it becomes impossible to reproduce spectral variations lower than this 10 nm interval, which could restrict the precision of spectral reconstructions for applications requiring finer resolution. Furthermore, our results are limited to the visible spectrum since the available data only cover this range. Therefore, extending our findings to other spectral domains, such as infrared, would be infeasible with current data. Future exploration in these areas would require acquiring additional data in the corresponding wavelength ranges. These aspects constitute essential limitations for the interpretation of the results of this study. Furthermore, the quality of the input data directly influences the results obtained. If the data used are of lower quality, with noise or artifacts, it becomes more difficult for models to accurately capture spectral and structural details.