A lightweight image denoising model based on feature knowledge distillation
To construct a lightweight image denoising model suitable for small-scale devices,a novel approach based on feature knowledge distillation was proposed in this paper.Deep-seated knowledge within a teacher model was captured through learning from its feature maps by this method,resulting in the creation of a lightweight denoising model with parameters only one-fifth the size of the teacher model.Experimental results validated the effectiveness of the distillation algorithm,demonstrating significant improvements in denoising performance for the student model across varying noise levels and datasets,which introduced a promising avenue for constructing lightweight image denoising models.
万方数据
维普
