Imaging through a Scattering Medium under Different Intensities of Ambient Light Interference

Many solutions for imaging through a scattering medium are sensitive to noise, which can lead to degradation or even to a failure of the image quality. This is especially the case in practical application scenarios, which are always filled with changing ambient light interference; as such, the traditional methods are difficult to practically apply. Therefore, in this paper, a spatial-frequency dual-domain learning neural network is designed for reconstructing the target of a speckle pattern under different intensities of ambient light interference. The network is mainly based on two modules. One module is designed from two perspectives, frequency domain denoising and the spatial-frequency spectrum of the speckle pattern. Another module is a dual-feature fusion attention module, which is used to improve the accuracy of the network. The experimental results demonstrate that the network is capable of reconstructing complex targets with high quality under varying intensities of interfering light. Furthermore, it is not constrained by the optical memory effect, exhibiting remarkable robustness and generalizability. The research based on this paper provides a feasible path for the practical application of scattering imaging methods.