1. A computer implemented method, comprising the steps of: computing, via one or more processors, a two-way optical flow between a reference frame and one or more associated frames; generating, via said or another one or more processors, a forward warping operator and a backward warping operator between the reference frame and each of the one or more associated frames in a warping module; computing, via said or another one or more processors, uncertainty of motion estimation weights for each of the forward warping operators and the backward warping operators in a weight module; and calculating, via said or another one or more processors, a super resolution iteration algorithm in a super-resolution iteration module, wherein the super resolution iteration algorithm is I H ( k + 1 ) = I H ( k ) + Δ ⁢ ⁢ t ⁡ ( ∑ n = 1 N ⁢ U n -> ref ⁢ W n -> ref ⁢ BD T ⁢ U ref -> n ⁢ ( I L n - DBW ref -> n ⁢ I H ( k ) ) + λ ⁢ ∂ R ⁡ ( ∇ I H ) ∂ I H ) , and wherein I L n , n =1.. . N is a set of collected low resolution images I H is a reconstructed high resolution image, D is a down sampling operator, D T is an upsampling operator, B is a blurring matrix, W is a warping matrix that describes scene motion, U is an uncertainty of motion estimation, ref is a reference frame and n is a warped frame such that indexes n → ref and ref → n denote an operation towards the reference frame and vice versa, Δt is iteration time step, λ is a regularization multiplier, R(∇I H ) is a regularization term, and k is the iteration step.