Algorithm 2: DECONVOLUTION([in] Ik (x), w(X), b [out] ˆI(X)).

$$\begin{array}{cc}\hfill & \mathbf{Input}:(i)I_k(\mathbf{x})-\text{The intensity of the frame}k\text{for}\hfill \\ \hfill & 1\le k\le K\text{in resolution limited image,and};(\text{ii})w(\mathbf{X})-\hfill \\ \hfill & \text{estimated PSF of entire super-resolution grid};\hfill \\ \hfill & (\text{iii})\text{Background intensity}-b.\hfill \\ \hfill & \mathbf{Output}:\widehat{I}(\mathbf{X})-\text{Mean (over}K\text{frames) of the estimated}\hfill \\ \hfill & \text{intensity in super-resolution image}.\hfill \\ \hfill & 1:\text{Initialize the estimate of the super-resolution image};\hfill \\ \hfill & {\widehat{I}}_k^1(\mathbf{X})=1\hfill \\ \hfill & 2:\mathbf{for}d=1:D\mathbf{do}\hfill \\ \hfill & \mathbf{a}.\text{Compute the ratio of the observed intensity to}\hfill \\ \hfill & \text{predicted intensity}\hfill \\ \hfill & e_k^d(\mathbf{X})=\frac{I_k^d(\mathbf{x})}{{\sum }_{\mathbf{x}\in \mathcal{R}}{\widehat{I}}_k^d(\mathbf{x})w(\mathbf{x}-\mathbf{X})+b}\hfill \\ \hfill & \text{for the entire image grids in low resolution images}.\hfill \\ \hfill & \mathbf{b}.\text{Estimate the sparsences parameter based on an}\hfill \\ \hfill & \text{exponential prior distribution of form}\hfill \\ \hfill & P[\widehat{I}]\propto \text{exp}(-\gamma \widehat{I})\hfill \\ \hfill & \mathbf{c}.\text{Update the sample estimate}\hfill \\ \hfill & {\widehat{I}}_k^{d+1}(\mathbf{X})=\hfill \\ \hfill & {\widehat{I}}_k^d(\mathbf{X}){\sum }_{\mathbf{x}\in \mathcal{R}}w(\mathbf{X}-\mathbf{x})e_k^d(\mathbf{x})∕(c(\mathbf{X})+\gamma ).\hfill \\ \hfill & 3:\mathrm{end}\mathrm{for}\hfill \end{array}$$