# Estimating the Spectral Response of Eight-Band MSFA One-Shot Cameras Using Deep Learning
## 1 Introduction  
Acquiring high-spectral-resolution multispectral images from low-resolution sensors represents a significant challenge in various fields, including remote sensing, medical imaging, precision agriculture, and scene analysis [1]. High-resolution multispectral images provide a more detailed and accurate analysis of observed scenes, which is important for environmental monitoring typo and disease-detection applications [2]. The acquisition of multispectral images through systems based on CRI, Hyspex, etc., provides images of 30 to bands, offering unparalleled information [3]. Each spectral band makes it possible to detect specific characteristics of objects that are not visible to the naked eye. As in precision agriculture, these images can help identify areas requiring particular treatments. They can detect tissue abnormalities in healthcare and find applications in many areas. However, its devices are not fast and poorly suited to slow-motion objects. 8-band one-shot MSFA (multispectral filter array) cameras provide a compact, cost-effective solution for spectraldata acquisition. However, they produce images of low spectral resolution. This band limitation makes capturing the details needed for in-depth analyses difficult. Therefore, although these sensors are accessible, the quality of the data they provide is insufficient for many critical applications. Thus, as part of the European CAVIAR project, which aims to improve image-detection technologies in various sectors, notably health, agriculture, etc., by relying on advanced imaging techniques, the use of an 8-band MSFA filter was used for the camera setup to capture a broader spectrum of light [4; 5]. However, the transition to a high-spectral-resolution system (31 to 100 bands) requires more sophisticated and expensive sensors, which are slow, limiting their accessibility. This is why estimating the spectral response of 8-band one-shot MSFA cameras raises significant technical challenges. In this context, deep learning methods are a promising solution. This study focuses on applying advanced deep learning techniques to improve the quality and accuracy of the spectral response of 8-band one-shot cameras to approximate hyperspectral systems. By harnessing the power of convolutional neural networks (CNN) and other deep neural-network architectures, it is possible to transform data captured by low-resolution sensors into high-quality multispectral images [6]. This study aims to demonstrate the potential of deep learning techniques to approximate the spectral response of 8-band one-shot cameras, making this technology more accessible and applicable to a wide range of domains. The results obtained open new perspectives for improving sensor systems and image analysis methods, thus contributing to significant advances in research and practical applications. The specific contributions of our study are as follows:  
* We have implemented image datasets that contain 8-band images as input and 31-band images as output. We retrieved the images from the TokyoTech [7] database, which offers 31-band multispectral images. Using a Gaussian projection, we generated 8-band images from this data. These images are then mosaicked and demosaicked to achieve a quality comparable to the camera developed as part of the CAVIAR project. This approach creates a robust dataset for training and evaluating models for estimating the spectral response of 8-band one-shot cameras. * We have developed several models based on deep-learning algorithms, including very deep super-resolution (VDSR), deeply recursive convolutional networks (DRCN), and Laplacian pyramid super-resolution networks (LapSRN). These models were initially used for super-resolution and then adapted to approximate the spectral response. These models are powerful tools for approximating the spectral response of 8-band one-shot MSFA cameras. By exploiting the advanced architectures of these networks, we aim to significantly improve the quality of the spectral response obtained with these cameras.