Deep Richardson-Lucy Deconvolution for Low-Light Image Deblurring
Liang Chen Jiawei Zhang Zhenhua Li Yunxuan Wei Faming Fang Jimmy Ren Jinshan Pan
Images taken under the low-light condition often contain blur and saturated
pixels at the same time. Deblurring images with saturated pixels is quite
challenging. Because of the limited dynamic range, the saturated pixels are
usually clipped in the imaging process and thus cannot be modeled by the linear
blur model. Previous methods use manually designed smooth functions to
approximate the clipping procedure. Their deblurring processes often require
empirically defined parameters, which may not be the optimal choices for
different images. In this paper, we develop a data-driven approach to model the
saturated pixels by a learned latent map. Based on the new model, the non-blind
deblurring task can be formulated into a maximum a posterior (MAP) problem,
which can be effectively solved by iteratively computing the latent map and the
latent image. Specifically, the latent map is computed by learning from a map
estimation network (MEN), and the latent image estimation process is
implemented by a Richardson-Lucy (RL)-based updating scheme. To estimate
high-quality deblurred images without amplified artifacts, we develop a prior
estimation network (PEN) to obtain prior information, which is further
integrated into the RL scheme. Experimental results demonstrate that the
proposed method performs favorably against state-of-the-art algorithms both
quantitatively and qualitatively on synthetic and real-world images.