  It is a challenging task to accurately perform semantic segmentation due to
the complexity of real picture scenes. Many semantic segmentation methods based
on traditional deep learning insufficiently captured the semantic and
appearance information of images, which put limit on their generality and
robustness for various application scenes. In this paper, we proposed a novel
strategy that reformulated the popularly-used convolution operation to
multi-layer convolutional sparse coding block to ease the aforementioned
deficiency. This strategy can be possibly used to significantly improve the
segmentation performance of any semantic segmentation model that involves
convolutional operations. To prove the effectiveness of our idea, we chose the
widely-used U-Net model for the demonstration purpose, and we designed CSC-Unet
model series based on U-Net. Through extensive analysis and experiments, we
provided credible evidence showing that the multi-layer convolutional sparse
coding block enables semantic segmentation model to converge faster, can
extract finer semantic and appearance information of images, and improve the
ability to recover spatial detail information. The best CSC-Unet model
significantly outperforms the results of the original U-Net on three public
datasets with different scenarios, i.e., 87.14% vs. 84.71% on DeepCrack
dataset, 68.91% vs. 67.09% on Nuclei dataset, and 53.68% vs. 48.82% on CamVid
dataset, respectively.
