# Estimating the Spectral Response of Eight-Band MSFA One-Shot Cameras Using Deep Learning
## 2 Related Work  
Several works have been carried out in the reconstruction of images, and we can cite, among others, [PERSON] et al., who developed a practical method for estimating the spectral response of camera sensors and the associated uncertainty using a specific imaging process. This method relies on a restricted set of 15 images, including diffraction images and color patches of known spectra, to obtain high-resolution spectral response estimates. The approach is versatile and applicable to any camera sensor in the visible domain. It does not impose strict constraints on the possible estimates, thus ensuring high precision of the results, which agree with previous estimates [8]. [PERSON] et al. conducted a study to test the performance of different sensor estimation techniques from the literature. They used measured and synthetic data, which they then compared to reference data to evaluate the effectiveness of these techniques on two cameras [9]. [PERSON] et al. proposed an estimation method based on a single image and exploiting fluorescence without requiring prior knowledge of the illumination spectrum. Under different light sources, the spectral distributions of the fluorescence emitted by the same material remain constant, up to one scale. Thus, the camera response to fluorescence maintains the same chromaticity. By relying on this chromatic invariance, they estimated the camera's spectral sensitivity under arbitrary illumination, the spectrum of which is unknown. Through numerous experiments, they demonstrated the accuracy of their method under various lighting conditions. Additionally, they showed how to recover daylight spectra from the estimated results and then use the cameras' estimated spectral sensitivities and daylight spectra to solve color correction problems [10]. [PERSON] et al. studied spectral features using singular value decomposition (SVD) to extract basis functions. They collected data from the literature and direct measurements of the sensitivity of different cameras. Their paper compares the extracted basis functions to identify the optimal set with other mathematical basis functions. These optimized basis functions can then be used to estimate the unknown spectral sensitivity of any camera [11]. [PERSON] et al. proposed a quick and straightforward method to obtain a reasonable approximation of camera-response functions using only a color chart and unknown, random lighting. This approach is made possible by carefully designing the cost function, which imposes several constraints to make the problem feasible. Among the components of this cost function, the Luther condition serves as a global shape prior, while commercial lighting systems with unknown spectra provide narrow spectral windows for local reconstruction. The quality of the rebuilding obtained is comparable to that of methods using known lighting [12]. [PERSON] et al. developed an innovative method to approximate the spectral response of color images. They introduced an approach based on neural networks, aiming to improve the two previous methods: those using circular and exponential functions and those based on the inverse of Penrose or Wiener. This new method can also be applied to calibrating most color scanning systems and subwavelength filters [13]. When we talk about a camera's spectral response, we also talk about the quality and reconstruction of the images. [PERSON] et al. thus conducted a comparative study on GANs from different angles to address these issues. They began by reviewing recent advances in GANs and then presented popular architectures suitable for image super-resolution, both for small and large samples. They analyzed the motivations, implementations, and differences of optimization methods based on GANs and discriminative learning, including supervised, semi-supervised, and unsupervised approaches. In addition, a comparison of the performance of these GANs was carried out on public datasets, accompanied by a quantitative and qualitative analysis [14]. For their part, [PERSON] et al.