alternating minimization strategy can be used to solve the model, which is described in Algorithm \[alg:IS\].

Parameters: $\lambda $ - sharpness parameter $\tau $ - subsample size parameter $\rho$ - subsample decay rate parameter Compute $I_{R}^0 = \dfrac{1}{n} \sum_{i=1}^n I_{TD}^{(i)}$ Compute the quality measure $\mathcal{Q} (I_{TD}^{(k)})$ for each video frame $\{I_{TD}^{(k)} \}_{k=1}^n$ Given $J^{t-1}, I_{R}^{t-1}$. Fixing $I_{R}^{t-1}$, solve $$J^t = \underset{J}{\arg\min} \dfrac{1}{\vert J \vert} \bigg(\sum_{k \in J} {\Big\Vert I_R^t - I_{TD}^{(k)} \Big\Vert}_2^2  + \lambda \mathcal{Q} (I_{TD}^{(k)}) \bigg)  - {\tau} \bigg(1 - e^{-\rho \vert J \vert} \bigg)$$ Compute $E_{1,k} = {\Big\Vert I_R^t - I_{TD}^{(k)} \Big\Vert}_2^2 +  \lambda \mathcal{Q} (I_{TD}^{(k)})$ for each $k$ and arrange $E_{1,k}$ in ascending order. Compute the sum $S_j$ for each $j$ and arrange $S_j$ in ascending order. $J^t \to \{k_1, k_2, \cdots, k_{j_1} \}$ Fixing $J^t$, solve $$I^t = \underset{I}{\arg\min} \dfrac{1}{\vert J^t \vert} \bigg(\sum_{k \in J^t} {\Big\Vert I_R - I_{TD}^{(k)} \Big\Vert}_2^2 \bigg)$$ $I_R^t \to \dfrac{1}{\vert J^t \vert} \sum_{k \in J^t} I_{TD}^{(k)}$ $E_1^{t-1} - E_1^{t} \leq \epsilon$\
subsampled image sequence $\{ I_{TD}^{(k)} \}$

### U-Net Architecture for Generator Network

The generator network $G$ we use is the U-Net [@ronneberger2015u], which consists of five types of layers: convolutional layer, deconvolutional layer, max-pooling layer, Randomized Leaky ReLU activation layer ($\alpha 