Restoration of images degraded by sensor non-linearity

In this paper, a new method based on the particle filtering concept is proposed for restoring images degraded by sensor non-linearity, blurring and noise. The approach is novel and leads to a development of the particle filter for space-variant image restoration problem. The key idea in our approach is to propagate samples corresponding to pixels in the state vector. These samples represent the true state density provided the number of samples is large enough. The interdependencies among the pixels is taken care of by the resampling stage of the algorithm. Our approach is recursive and can handle non-linear/non-Gaussian situations also. This is unlike the Kalman filter which is also recursive in nature but works well only under linear and Gaussian conditions. Also, the particle filter is considerably simpler to implement than the Kalman filter. The proposed method is validated on real images degraded by space-invariant as well as space-variant blur in the presence of sensor non-linearity and noise.