Frequency aware high-quality computer-generated holography via multilevel wavelet learning and channel attention

Deep learning-based computer-generated holography offers significant advantages for real-time holographic displays. Most existing methods typically utilize convolutional neural networks (CNNs) as the basic framework for encoding phase-only holograms (POHs). However, recent studies have shown that CNNs suffer from spectral bias, resulting in insufficient learning of high-frequency components. Here, we propose a novel, to our knowledge, frequency aware network for generating high-quality POHs. A multilevel wavelet-based channel attention network (MW-CANet) is designed to address spectral bias. By employing multi-scale wavelet transformations, MW-CANet effectively captures both low- and high-frequency features independently, thus facilitating an enhanced representation of high-frequency information crucial for accurate phase inference. Furthermore, MW-CANet utilizes an attention mechanism to discern and allocate additional focus to critical high-frequency components. Simulations and optical experiments confirm the validity and feasibility of our method.