Satellite image restoration using RLS adaptive filter and enhancement by image processing techniques

Satellite images in course of capturing and transmitting are frequently degraded due to channel effects or uncertain conditions. These effects introduce different noise patterns such as, Additive White Gaussian Noise, Salt & Pepper Noise and Mixed Noise. Therefore, retrieved images are highly noise corrupted because the image contents are more attenuated or amplified. The selection of optimum image restoration and filtering technique depends to have knowledge about the characteristics of degrading system and noise pattern in an image. In this paper, Recursive Least Square (RLS) adaptive algorithm is used for image restoration from highly noise corrupted images. The implementation of proposed methodology is being carried out by estimating the noise patterns of wireless channel through configuring System Identification with RLS adaptive algorithm. Then, these estimated noise patterns are eliminated by configuring Signal Enhancement with RLS algorithm. The restored images are functioned for further denoising and enhancement techniques. The image restoration and further processing algorithms are simulated in MATLAB environment. The performance is evaluated by means of Human Visual System, quantitative measures in terms of MSE, RMSE, SNR & PSNR and by graphical measures. The experimental results demonstrate that RLS adaptive algorithm efficiently eliminated noise from distorted images and delivered a virtuous evaluation without abundant degradation in performance.