for each video frame $I_{TD}^{(k)}$ is based on the normalized version of ${\Vert \Delta I_k \Vert}_1$: $$\mathcal{Q}(I_{TD}^{(k)}) =  \dfrac{\underset{1\leq i \leq n}{\max} {\Vert \Delta I_{TD}^{(i)}  \Vert}_1 - {\Vert \Delta I_{TD}^{(k)} \Vert}_1}{\underset{1\leq i \leq n}{\max} {\Vert \Delta I_{TD}^{(i)}  \Vert}_1 - \underset{1\leq i \leq n}{\min} {\Vert \Delta I_{TD}^{(i)}  \Vert}_1 }$$ The term $\Delta I_{TD}^{(k)}$ is the convolution of $I_{TD}^{(k)}$ with the Laplacian kernel specifically highlighting the edges and features of objects in the image $I_{TD}^{(k)}$. The sharper the image $I_{TD}^{(k)}$, the higher the magnitude of $\Delta I_{TD}^{(k)}$. As a consequence, the normalized quality measure $\mathcal{Q}(I_{TD}^{(k)})$ is smaller when $I_{TD}^{(k)}$ is sharp. The term $\lambda$ in the energy model $E(I_R,J)$ is a positive constant to quantify the importance of sharpness of the frame $I_{TD}^{(k)}$. The regularization term $\mathcal{R}$ is the concave increasing function $1 - e^{-\rho \vert J \vert}$, where $\rho > 0$ is a constant to quantify the importance of the number of selected frames. The function is chosen in order to acquire more information from additional video frames, whereas the effect on the quality of the reference image $I_R$ is reduced with a marginal increase in the subsample size. The detailed formulation of the variational model is described in [@lau2017variational]. An 