Generative approach for lensless imaging in low-light conditions.

Lensless imaging offers a lightweight, compact alternative to traditional lens-based systems, ideal for exploration in space-constrained environments. However, the absence of a focusing lens and limited lighting in such environments often results in low-light conditions, where the measurements suffer from complex noise interference due to insufficient capture of photons. This study presents a robust reconstruction method for high-quality imaging in low-light scenarios, employing two complementary perspectives: model-driven and data-driven. First, we apply a physics-model-driven perspective to reconstruct the range space of the pseudo-inverse of the measurement model-as a first guidance to extract information in the noisy measurements. Then, we integrate a generative-model-based perspective to suppress residual noises-as the second guidance to suppress noises in the initial noisy results. Specifically, a learnable Wiener filter-based module generates an initial, noisy reconstruction. Then, for fast and, more importantly, stable generation of the clear image from the noisy version, we implement a modified conditional generative diffusion module. This module converts the raw image into the latent wavelet domain for efficiency and uses modified bidirectional training processes for stabilization. Simulations and real-world experiments demonstrate substantial improvements in overall visual quality, advancing lensless imaging in challenging low-light environments.