  Existing fixed pattern noise reduction (FPNR) methods are easily affected by
the motion state of the scene and working condition of the image sensor, which
leads to over smooth effects, ghosting artifacts as well as slow convergence
rate. To address these issues, we design an innovative cascade convolution
neural network (CNN) model with residual skip connections to realize single
frame blind FPNR operation without any parameter tuning. Moreover, a
coarse-fine convolution (CF-Conv) unit is introduced to extract complementary
features in various scales and fuse them to pick more spatial information.
Inspired by the success of the visual attention mechanism, we further propose a
particular spatial-channel noise attention unit (SCNAU) to separate the scene
details from fixed pattern noise more thoroughly and recover the real scene
more accurately. Experimental results on test data demonstrate that the
proposed cascade CNN-FPNR method outperforms the existing FPNR methods in both
of visual effect and quantitative assessment.
