Noisy Remote Sensing Image Fusion Based on JSR

Abstract

Compressed sensing has shown great potential and power in image representation, especially in image reconstruction by sparse representation. Due to complementary information and unavoidable noise existing in synthetic aperture radar (SAR) and other source images, joint sparse representation (JSR) is developed to separate redundancy and complementary information with different properties in source images and obtain a fused image, where image de-noising is done simultaneously owing to that noise is not sparse and cannot be represented by sparse representation. As a result, one noisy remote sensing image fusion method based on JSR is presented in this paper. After obtaining redundant and complementary sub-images by JSR, an improved fusion rule based on pulse coupled neural network (PCNN) is employed to fuse complementary sparse coefficients together. At the same time, because the types of noise in SAR and other source images are different, they can be treated as the complementary information in source images and suppressed at this step. Finally, a fused image can be reconstructed by adding the redundant and fused complementary sub-images. Quantitative and qualitative experimental results show that the proposed method outperforms most of other fusion methods and it is more robust to noise, having better visual effects and values of objective evaluation metrics.