An alternative SR spatial enhancement based on adaptive meridian filter and GOM registration for severe noisy blurred videos

Commonly, filtering technique and the video registration technique are two main significance factors of a video SR (Super Resolution) enhancement algorithm. First, the classical filtering technique is based on a linear filter such as mean or median filter that are only suitable for noiseless or low power noise. Later, classical video registration techniques are usually based on a simple translation model because of the fast computation and easy implementation thereby this registration has high precision error. To get over both problems, this paper proposed the alternative SR spatial enhancement using adaptive meridian filter and GOM (General Observation Model) registration for severe noisy blurred videos. The adaptive meridian filter is a robust filter, which is desire for controlling high power outlier, and GOM is a high precision registration technique, which is desired for registering a fast spatial sequence. For proving the proposed performance, the simulated experiments are done in several environments as following: 1. Additive White Gaussian Noise (AWGN) at SNR=15, 17.5, 20, 22.5, 25dB; 2. Poisson Noise; 3. Multiplicative White Gaussian Noise (Speckle Noise) at V=0.01, 0.02, 0.03; 4. Salt and Pepper Noise at D=0.005, 0.010, 0.015. The proposed enhancement algorithm shows that the PSRN of the enhanced image is higher than the SR spatial enhancement based on classical filter with classical registration and GOM registration.