Abstract: Limited by the environment and the acquisition device, the captured images are susceptible to the noise and this leads to the poor visual perception for the users. To systematically remove noise from the images, an end-to-end Jacobian approximation denoising network algorithm is proposed. Specifically, an ordinary differential equation is utilized to construct a forward differential structure for dynamically simulating the noise distribution in the image. A Jacobian matrix-based solution module is designed to implement the forward derivative and reduce the complexity of the denoising network. To enhance the feature representation capability for complex noise, a multi-scale feature extraction module is designed to capture the features of non-uniform noise. Besides, a dual attention structure is used to enhance the reconstructed features and improve the quality of the reconstructed images. Extensive experimental results demonstrate the effectiveness of the proposed algorithm on eliminating synthetic and real noise from images, and the reconstructed image achieves better results in both subjective visual effect and objective evaluation metrics.

Key words: ordinary differential equation ,  image denoising ,   Jacobian solution module ,   multi-scale feature extraction module ,   convolutional neural network