Deep learning enhanced quantum dot mosaic snapshot spectral imaging system

Spectrum, as the 'fingerprint' of materials, reveals their composition and properties. However, traditional spectral imaging systems are hindered by their bulkiness, high cost, time-consuming and high complexity, limiting their widespread use. By combining quantum dot (QD) spectral sensing with multi-spectral filter array designs, QD mosaic snapshot spectral imaging provides a real-time, compact, low-cost, and low-complexity alternative to traditional spectral imaging systems. Yet, the high flexibility of QD response curves presents a significant challenge: how to achieve an efficient, principle-guided, and data-adaptive design of high reconstruction performance? In this work, we propose a two-stage design method. Firstly, a set of absorption spectra with distinct particle sizes is selected using QR decomposition. Then, by leveraging the similarity between the imaging model and the convolutional operation, we optimize the concentration of QDs via gradient descent during the training stage of the reconstruction network, which serves as the software decoder to recover the spectral images from the encoded measurements. The proposed method is validated on the CAVE and Harvard datasets across multiple cases, achieving up to a 6.82% improvement in PSNR over the baseline, along with consistent gains in SSIM and SAM metrics. These results confirm the effectiveness of the proposed principle-guided method in achieving high reconstruction performance for spectral imaging systems.