Denoising for Photon-limited Imaging via a Multi-level Pyramid Network

Imaging under photon-scarce situations introduces challenges to many applications as the captured images are with low signal-to-noise ratio. Here, we target on the denoising under photon-limited imaging. We develop a multi-level pyramid denoising network (MPDNet), which employs the idea of Laplacian pyramid to learn the small-scale noise map and larger-scale high-frequency details at different levels. Feature extractions are conducted on the multi-scale input images to encode richer contextual information. The major component of MPDNet is the multi-skip attention residual block, which integrates multi-scale feature fusion and attention mechanism for better feature representation. Experimental results have demonstrated that our MPDNet can achieve superior denoising performance on the images with low photon counts.