Wide-field imaging and recognition through cascaded complex scattering media

Considering the obvious application value in the field of minimally invasive and non-destructive clinical healthcare, we explore the challenge of wide-field imaging and recognition through cascaded complex scattering media, a topic that has been less researched, by realizing wide-field imaging and pathological screening through multimode fibers (MMF) and turbid media. To address the challenge of extracting features from chaotic and globally correlated speckles formed by transmitting images through cascaded complex scattering media, we establish a deep learning approach based on SMixerNet. By efficiently using the parameter-free matrix transposition, SMixerNet achieves a broad receptive field with less inductive bias through concise multi-layer perceptron (MLP). This approach circumvents the parameter's intensive requirements of previous implementations relying on self-attention mechanisms for global receptive fields. Imaging and pathological screening results based on extensive datasets demonstrate that our approach achieves better performance with fewer learning parameters, which helps deploy deep learning models on desktop-level edge computing devices for clinical healthcare. Our research shows that, deep learning facilitates imaging and recognition through cascaded complex scattering media. This research extends the scenarios of medical and industrial imaging, offering additional possibilities in minimally invasive and non-destructive clinical healthcare and industrial monitoring in harsh and complex scenarios.