A Secure Outsourced Fusion Denoising Scheme in Multiple Encrypted Remote Sensing Images

Remote sensing image denoising is a hot research topic in the field of image processing. The improvement of remote sensing image acquisition equipment and technology has made it possible to collect multiple images from the same scene in a short period of time. However, the processing huge number of the remote sensing images on the ordinary computers has caused the low processing capability and poor concurrency. It is a trend to store and compute the big data outsourced to the cloud. To protect the security of outsourced remote sensing images, the article presents a secure outsourced fusion denoising scheme in multiple encrypted remote sensing images to implement the fusion denoising based on dynamic filtering parameters. In the schemes, the ciphertext from Johnson-Lindenstrauss transform is used to weight calculatation as well as the plaintext and the ciphertext from Paillier homomorphic encryption is used to fusion denoise by the linear calculation of ciphertext. The experiments use several 512×512 pixels remote sensing images based on the Spark alone-server environment to simulate the cloud platform. The experimental results show that the outsourcing schemes can effectively ensure the security of the remote sensing images and get better denoising quality with different sizes of noise than the existing schemes.