Algorithm 1.

Cluster-based Speckle Reduction Framework (CSRF)

Initialization             $I\left[i,j\right]$	intensity of a pixel at row $i$ and column $j$ in the original OCT image
$AC\left[i,j\right]$	The estimated attenuation coefficient of a pixel at row $i$ and column $j$ in the original OCT image
$\Delta$	pixel size in mm
$R$	number of pixels in each A-line (number of rows)
$C$	number of A-lines (number of columns)
$N$	total number of pixels in the OCT image $\left(R\times C\right)$
$F$	feature vector including ($I\left[i,j\right]$, $AC\left[i,j\right]$)
$K$	desired number of clusters
$CL\_KM$	KM clustering result
$H_{i,j}$	sliding window around pixel $\left[i,j\right]$ in the original image
$N1\times N2$	size of sliding window
$W_{i,j}$	Weight matrix for pixels in $H_{i,j}$
$n_l$	Number of pixels in $H_{i,j}$ that belong to the same cluster as the central pixel
$1_{3\ast 3}$	A matrix of ones with size 3*3
$Average\left(data,weights\right)=1/N_d{\displaystyle \sum data\times weight}$
1. Feature Extraction  1.1. For each pixel in position $\left[i,j\right]$, calculate and assign a set of features. We only use the attenuation coefficient [38]: $AC\left[i,j\right]=\frac{1}{2\Delta }\ln \left(1+\frac{I\left[i,j\right]}{{\displaystyle {\sum }_{z=i+1}^{\infty }I\left[z,j\right]}}\right)$  1.2. For each pixel at position $\left[i,j\right]$, assign the feature vector $F\left[i,j\right]$: $F\left[i,j\right]=\left(I\left[i,j\right],AC\left[i,j\right]\right)$ 2. Clustering  2.1. Apply KM clustering which is $CL\_KM=KM\left(F,K\right)$ $CL\_KM=Median\_Filter\left(CL\_KM\right)$ 3. Apply Filtering: for a sliding window of size $N1\times N2$ around pixel $\left[i,j\right]$, $H_{i,j}$  2.2. Apply median filter on the clustering results: $CL\_KM=Median\_Filter\left(CL\_KM\right)$ 3. Apply Filtering: for a sliding window of size $N1\times N2$ around pixel $\left[i,j\right]$, $H_{i,j}$  3.1. Estimate the noise variance for each individual cluster ${{\sigma }_{nk}}^2$  3.2. Compute the weight matrix $W_{i,j}$ for all the pixels in $H_{i,j}$. The elements of $W_{i,j}$, $\omega \left(i,j,p,q\right)$are determined as follows:   3.2.1. Lee filter (LF): all the pixels in the window are weighted the same $\omega \left(i,j,p,q\right)=1/N1\times N2$   3.2.2. Jin’s adaptive Wiener filter (AWF) $\omega \left(i,j,p,q\right)=\frac{K\left(i,j\right)}{1+a\left(\max \left[{\in }^2,{\left(I\left(i,j\right)-H_{i,j}\left(p,q\right)\right)}^2\right]\right)}$   3.2.3. Cluster-based LF (CSRF-LF) $\omega \left(i,j,p,q\right)=\{\begin{array}{l}1,L\left(p,q\right)=L\left(i,j\right)\\ 0,L\left(p,q\right)\ne L\left(i,j\right)\text{or}\left(p,q\right)=\left(i,j\right)\end{array}$   3.2.4. Cluster-based adaptive Wiener filter (CSRF-AWF) $\omega \left(i,j,p,q\right)=\frac{K\left(i,j\right)}{\{\begin{array}{l}1+a\left(\max \left[{\in }^2,{\left(I\left(i,j\right)-H_{i,j}\left(p,q\right)\right)}^2\right]\right),|L\left(p,q\right)=L\left(i,j\right)\\ 0,|L\left(p,q\right)\ne L\left(i,j\right)or\left(p,q\right)=\left(i,j\right)\end{array}}$  3.3. For each window, calculate the local statistics: ${\mu }_{i,j}=Average\left(H_{i,j},W_{i,j}\right)$ ${{\sigma }_{i,j}}^2=Average\left(H_{i,j}-\mu {\left[i,j\right]}^2,W_{i,j}\right)$  3.4. Calculate the new pixel value: $\hat{I}\left(i,j\right)={\mu }_x+\frac{{{\sigma }_x}^2}{{{\sigma }_x}^2+{{\sigma }_n}^2}\left(I_{\left(i,j\right)}-{\mu }_x\right)$ 4. Smoothing the cluster edges with a 3 × 3 Mean filter: $\hat{I}\left(i,j\right)=Average\left(H_{i,j},1_{3\ast 3}\right)$