MSFA Image Denoising Using Physics-Based Noise Model and Noise-Decoupled Network

Multispectral filter array (MSFA) camera is increasingly used due to its compact size and fast capturing speed. However, because of its narrow-band property, it often suffers from the light-deficient problem, and images captured are easily overwhelmed by noise. As a type of commonly used denoising method, neural networks have shown their power to achieve satisfactory denoising results. However, their performance highly depends on high-quality noisy-clean image pairs. For the task of MSFA image denoising, there is currently neither a paired real dataset nor an accurate noise model capable of generating realistic noisy images. To this end, we present a physics-based noise model that is capable to match the real noise distribution and synthesize realistic noisy images. In our noise model, those different types of noise can be divided into SimpleDist component and ComplexDist component. The former contains all the types of noise that can be described using a simple probability distribution like Gaussian or Poisson distribution, and the latter contains the complicated color bias noise that cannot be modeled using a simple probability distribution. Besides, we design a noise-decoupled network consisting of a SimpleDist noise removal network (SNRNet) and a ComplexDist noise removal network (CNRNet) to sequentially remove each component. Moreover, according to the non-uniformity of color bias noise in our noise model, we introduce a learnable position embedding in CNRNet to indicate the position information. To verify the effectiveness of our physics-based noise model and noise-decoupled network, we collect a real MSFA denoising dataset with paired long-exposure clean images and short-exposure noisy images. Experiments are conducted to prove that the network trained using synthetic data generated by our noise model performs as well as trained using paired real data, and our noise-decoupled network outperforms other state-of-the-art denoising methods.