Spectral imaging technology has gained widespread application across diverse fields due to its ability to capture spatial and spectral information simultaneously. However, conventional spectral scanning methods using single-peak tunable filters face the challenges of low optical throughput. Inspired by Fellgett's advantage in Fourier-transform infrared spectroscopy, this paper proposes a tunable filter with multiple resonances to improve optical throughput. It is composed of a simple Fabry-Pérot cavity filled with liquid crystal. An artificial neural network is employed to match with the filter for spectrum reconstruction. Experimental results show a spectral resolution of 10 nm and a switching time of ≈23 ms between adjacent states. As a demonstration, biological specimens are spectrally imaged under different light conditions with good fidelity. The results suggest that the filter possesses over six times higher optical throughput than a commercial liquid crystal tunable filter (LCTF), leading to better spectrum accuracy for spectral imaging under low-light conditions. The compact and cost-effective design of this tunable filter enables seamless integration into imaging systems, presenting promising prospects for practical applications such as portable health management and food inspection in low-light conditions.