Remotely Sensed Image Restoration Using Partial Differential Equations and Watershed Transformation

This paper proposes a novel approach for remotely sensed image restoration. The main goal of this study is to mitigate two most well-known types of noises from remote sensing images while their important details such as edges are preserved. To this end, a novel method based on partial differential equations is proposed. The parameters used in the proposed algorithm are adaptively set regarding the type of noise and the texture of noisy datasets. Moreover, we propose to apply a segmentation pre-processing step based on Watershed transformation to localize the denoising process. The performance of the restoration techniques is measured using PSNR criterion. For further assessment, we also feed the original/noisy/denoised images into SVM classifier and explore the results.