A novel approach for denoising coloured remote sensing image using Legendre Fenchel Transformation

Data acquired from remote sensing satellites are processed in order to retrieve the information from an image. Those images are preprocessed using image processing techniques such as noise removal. Satellite images are assumed to be corrupted with white Gaussian noise of zero mean and constant variance. Three planes of the noisy image are denoised separately through Legendre Fenchel Transformation. Later, these three planes are concatenated and compared with results obtained by Euler-Lagrange ROF model. Simulation results show that Legendre Fenchel ROF is highly convergent and less time consuming. To add evidence to the outcomes, quality metrics such as variance and PSNR for noisy and denoised images are calculated. The qualitative analysis of an image is analysed using MSSIM calculations, which clarifies the Structural Similarity between denoised images with original image.