# Estimating the Spectral Response of Eight-Band MSFA One-Shot Cameras Using Deep Learning
## 7 Conclusions  
Spectral response evaluation of 8-band one-shot MSFA (multispectral filter array) cameras is an innovative research area combining multispectral imaging and advanced deep-learning techniques. In this study, we developed models based on VDSR, DRCN, and LapSRN algorithms, adapted to approximate the spectral response of 8-band one-shot MSFA cameras. We observed significant differences in performance and assessed using key metrics such as loss, PSNR, SSIM, SAM, RMSE, and MAE. The DRCN model performed best, achieving superior results on all metrics considered. Its ability to reduce reconstruction error while preserving the structural details of images makes it a preferred choice for applications requiring high image quality. On the other hand, the VDSR and LapSRN models, although efficient, show lower performance, which could limit their use in contexts requiring optimal quality. Our study allowed us to develop models to estimate the spectral response of 8-band one-shot MSFA cameras into 31 bands. It is essential to continue this work to improve the quality of the spectral response because the quality of the original images significantly influences the results obtained. The future of image processing lies in developing models that combine performance, efficiency, and adaptability to various situations, ensuring continuous improvement in image quality in different application areas. Future research should focus on improving the input data, optimizing the models used, and exploring other models to find a better balance between performance and efficiency. For future perspectives, the next step is to validate our approach to the camera developed as part of the CAVIAR project. For this purpose, a database is currently being prepared. Conceptualization, K.J.A. and P.G. ; methodology, K.J.A. and D.M. ; software, K.J.A. ; validation, P.G. and D.M. ; formal analysis, K.J.A. ; investigation, K.J.A. and D.M. ; resources, K.J.A. and P.G. ; data curation, K.J.A. ; writing--original draft preparation, K.J.A. and D.M. ; writing--review and editing, P.G. ; visualization, D.M. ; supervision, P.G. and D.M. ; project administration, P.G. ; funding acquisition, P.G. All authors have read and agreed to the published version of the manuscript. This research received no external funding. [1][[https://www.ok.sc.e.titech.ac.jp/res/MSI/MSIdata31.html](https://www.ok.sc.e.titech.ac.jp/res/MSI/MSIdata31.html)]([https://www.ok.sc.e.titech.ac.jp/res/MSI/MSIdata31.html](https://www.ok.sc.e.titech.ac.jp/res/MSI/MSIdata31.html)) (accessed date 14 October 2024). [2][[https://www.ok.sc.e.titech.ac.jp/res/MSI/MSIdata31.html](https://www.ok.sc.e.titech.ac.jp/res/MSI/MSIdata31.html)]([https://www.ok.sc.e.titech.ac.jp/res/MSI/MSIdata31.html](https://www.ok.sc.e.titech.ac.jp/res/MSI/MSIdata31.html)) (accessed date 14 October 2024). [3][[https://www.ok.sc.e.titech.ac.jp/res/MSI/MSIdata31.html](https://www.ok.sc.e.titech.ac.jp/res/MSI/MSIdata31.html)]([https://www.ok.sc.e.titech.ac.jp/res/MSI/MSIdata31.html](https://www.ok.sc.e.titech.ac.jp/res/MSI/MSIdata31.html)) (accessed date 14 October 2024). [4][[https://www.ok.sc.e.titech.ac.jp/res/MSI/MSIdata31.html](https://www.ok.sc.e.titech.ac.jp/res/MSI/MSIdata31.html)]([https://www.ok.sc.e.titech.ac.jp/res/MSI/MSIdata31.html](https://www.ok.sc.e.titech.ac.jp/res/MSI/MSIdata31.html)) (accessed date 14  
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