diff --git a/eis_toolkit/cli.py b/eis_toolkit/cli.py
index ae78b62d..c92246c8 100644
--- a/eis_toolkit/cli.py
+++ b/eis_toolkit/cli.py
@@ -200,6 +200,27 @@ class NodataHandling(str, Enum):
     remove = "remove"
 
 
+class FocalFilterMethod(str, Enum):
+    """Focal filter methods."""
+
+    mean = "mean"
+    median = "median"
+
+
+class FocalFilterShape(str, Enum):
+    """Shape of the filter window."""
+
+    square = "square"
+    circle = "circle"
+
+
+class MexicanHatFilterDirection(str, Enum):
+    """Direction of calculating kernel values."""
+
+    rectangular = "rectangular"
+    circular = "circular"
+
+
 class LocalMoranWeightType(str, Enum):
     """Weight type for Local Moran's I."""
 
@@ -533,6 +554,276 @@ def local_morans_i_cli(
 # --- RASTER PROCESSING ---
 
 
+# FOCAL FILTER
+@app.command()
+def focal_filter_cli(
+    input_raster: Annotated[Path, INPUT_FILE_OPTION],
+    output_raster: Annotated[Path, OUTPUT_FILE_OPTION],
+    method: FocalFilterMethod = FocalFilterMethod.mean,
+    size: int = 3,
+    shape: FocalFilterShape = FocalFilterShape.circle,
+):
+    """Apply a basic focal filter to the input raster."""
+    from eis_toolkit.raster_processing.filters.focal import focal_filter
+
+    typer.echo("Progress: 10%")
+
+    with rasterio.open(input_raster) as raster:
+        typer.echo("Progress: 25%")
+        out_image, out_meta = focal_filter(raster=raster, method=method, size=size, shape=shape)
+    typer.echo("Progress: 75%")
+
+    with rasterio.open(output_raster, "w", **out_meta) as dest:
+        dest.write(out_image, 1)
+    typer.echo("Progress: 100%")
+
+    typer.echo(f"Focal filter applied, output raster written to {output_raster}.")
+
+
+# GAUSSIAN FILTER
+@app.command()
+def gaussian_filter_cli(
+    input_raster: Annotated[Path, INPUT_FILE_OPTION],
+    output_raster: Annotated[Path, OUTPUT_FILE_OPTION],
+    sigma: float = 1.0,
+    truncate: float = 4.0,
+    size: int = None,
+):
+    """Apply a gaussian filter to the input raster."""
+    from eis_toolkit.raster_processing.filters.focal import gaussian_filter
+
+    typer.echo("Progress: 10%")
+
+    with rasterio.open(input_raster) as raster:
+        typer.echo("Progress: 25%")
+        out_image, out_meta = gaussian_filter(raster=raster, sigma=sigma, truncate=truncate, size=size)
+    typer.echo("Progress: 75%")
+
+    with rasterio.open(output_raster, "w", **out_meta) as dest:
+        dest.write(out_image, 1)
+    typer.echo("Progress: 100%")
+
+    typer.echo(f"Gaussial filter applied, output raster written to {output_raster}.")
+
+
+# MEXICAN HAT FILTER
+@app.command()
+def mexican_hat_filter_cli(
+    input_raster: Annotated[Path, INPUT_FILE_OPTION],
+    output_raster: Annotated[Path, OUTPUT_FILE_OPTION],
+    sigma: float = 1.0,
+    truncate: float = 4.0,
+    size: int = None,
+    direction: MexicanHatFilterDirection = MexicanHatFilterDirection.circular,
+):
+    """Apply a mexican hat filter to the input raster."""
+    from eis_toolkit.raster_processing.filters.focal import mexican_hat_filter
+
+    typer.echo("Progress: 10%")
+
+    with rasterio.open(input_raster) as raster:
+        typer.echo("Progress: 25%")
+        out_image, out_meta = mexican_hat_filter(
+            raster=raster, sigma=sigma, truncate=truncate, size=size, direction=direction
+        )
+    typer.echo("Progress: 75%")
+
+    with rasterio.open(output_raster, "w", **out_meta) as dest:
+        dest.write(out_image, 1)
+    typer.echo("Progress: 100%")
+
+    typer.echo(f"Mexican hat filter applied, output raster written to {output_raster}.")
+
+
+# LEE ADDITIVE NOISE FILTER
+@app.command()
+def lee_additive_noise_filter_cli(
+    input_raster: Annotated[Path, INPUT_FILE_OPTION],
+    output_raster: Annotated[Path, OUTPUT_FILE_OPTION],
+    size: int = 3,
+    add_noise_var: float = 0.25,
+):
+    """Apply a Lee filter considering additive noise components in the input raster."""
+    from eis_toolkit.raster_processing.filters.speckle import lee_additive_noise_filter
+
+    typer.echo("Progress: 10%")
+
+    with rasterio.open(input_raster) as raster:
+        typer.echo("Progress: 25%")
+        out_image, out_meta = lee_additive_noise_filter(raster=raster, size=size, add_noise_var=add_noise_var)
+    typer.echo("Progress: 75%")
+
+    with rasterio.open(output_raster, "w", **out_meta) as dest:
+        dest.write(out_image, 1)
+    typer.echo("Progress: 100%")
+
+    typer.echo(f"Additive Lee noise filter applied, output raster written to {output_raster}.")
+
+
+# LEE MULTIPLICATIVE NOISE FILTER
+@app.command()
+def lee_multiplicative_noise_filter_cli(
+    input_raster: Annotated[Path, INPUT_FILE_OPTION],
+    output_raster: Annotated[Path, OUTPUT_FILE_OPTION],
+    size: int = 3,
+    multi_noise_mean: float = 1.0,
+    n_looks: int = 1,
+):
+    """Apply a Lee filter considering multiplicative noise components in the input raster."""
+    from eis_toolkit.raster_processing.filters.speckle import lee_multiplicative_noise_filter
+
+    typer.echo("Progress: 10%")
+
+    with rasterio.open(input_raster) as raster:
+        typer.echo("Progress: 25%")
+        out_image, out_meta = lee_multiplicative_noise_filter(
+            raster=raster, size=size, mult_noise_mean=multi_noise_mean, n_looks=n_looks
+        )
+    typer.echo("Progress: 75%")
+
+    with rasterio.open(output_raster, "w", **out_meta) as dest:
+        dest.write(out_image, 1)
+    typer.echo("Progress: 100%")
+
+    typer.echo(f"Multiplicative Lee noise filter applied, output raster written to {output_raster}.")
+
+
+# LEE ADDITIVE MULTIPLICATIVE NOISE FILTER
+@app.command()
+def lee_additive_multiplicative_noise_filter_cli(
+    input_raster: Annotated[Path, INPUT_FILE_OPTION],
+    output_raster: Annotated[Path, OUTPUT_FILE_OPTION],
+    size: int = 3,
+    add_noise_var: float = 0.25,
+    add_noise_mean: float = 0,
+    multi_noise_mean: float = 1.0,
+):
+    """Apply a Lee filter considering both additive and multiplicative noise components in the input raster."""
+    from eis_toolkit.raster_processing.filters.speckle import lee_additive_multiplicative_noise_filter
+
+    typer.echo("Progress: 10%")
+
+    with rasterio.open(input_raster) as raster:
+        typer.echo("Progress: 25%")
+        out_image, out_meta = lee_additive_multiplicative_noise_filter(
+            raster=raster,
+            size=size,
+            add_noise_var=add_noise_var,
+            add_noise_mean=add_noise_mean,
+            mult_noise_mean=multi_noise_mean,
+        )
+    typer.echo("Progress: 75%")
+
+    with rasterio.open(output_raster, "w", **out_meta) as dest:
+        dest.write(out_image, 1)
+    typer.echo("Progress: 100%")
+
+    typer.echo(f"Additive multiplicative Lee noise filter applied, output raster written to {output_raster}.")
+
+
+# LEE ENHANCED FILTER
+@app.command()
+def lee_enhanced_filter_cli(
+    input_raster: Annotated[Path, INPUT_FILE_OPTION],
+    output_raster: Annotated[Path, OUTPUT_FILE_OPTION],
+    size: int = 3,
+    n_looks: int = 1,
+    damping_factor: float = 1.0,
+):
+    """Apply an enhanced Lee filter to the input raster."""
+    from eis_toolkit.raster_processing.filters.speckle import lee_enhanced_filter
+
+    typer.echo("Progress: 10%")
+
+    with rasterio.open(input_raster) as raster:
+        typer.echo("Progress: 25%")
+        out_image, out_meta = lee_enhanced_filter(
+            raster=raster, size=size, n_looks=n_looks, damping_factor=damping_factor
+        )
+    typer.echo("Progress: 75%")
+
+    with rasterio.open(output_raster, "w", **out_meta) as dest:
+        dest.write(out_image, 1)
+    typer.echo("Progress: 100%")
+
+    typer.echo(f"Enhanced Lee filter applied, output raster written to {output_raster}.")
+
+
+# GAMMA FILTER
+@app.command()
+def gamma_filter_cli(
+    input_raster: Annotated[Path, INPUT_FILE_OPTION],
+    output_raster: Annotated[Path, OUTPUT_FILE_OPTION],
+    size: int = 3,
+    n_looks: int = 1,
+):
+    """Apply a Gamma filter to the input raster."""
+    from eis_toolkit.raster_processing.filters.speckle import gamma_filter
+
+    typer.echo("Progress: 10%")
+
+    with rasterio.open(input_raster) as raster:
+        typer.echo("Progress: 25%")
+        out_image, out_meta = gamma_filter(raster=raster, size=size, n_looks=n_looks)
+    typer.echo("Progress: 75%")
+
+    with rasterio.open(output_raster, "w", **out_meta) as dest:
+        dest.write(out_image, 1)
+    typer.echo("Progress: 100%")
+
+    typer.echo(f"Gamma filter applied, output raster written to {output_raster}.")
+
+
+# FROST FILTER
+@app.command()
+def frost_filter_cli(
+    input_raster: Annotated[Path, INPUT_FILE_OPTION],
+    output_raster: Annotated[Path, OUTPUT_FILE_OPTION],
+    size: int = 3,
+    damping_factor: float = 1.0,
+):
+    """Apply a Frost filter to the input raster."""
+    from eis_toolkit.raster_processing.filters.speckle import frost_filter
+
+    typer.echo("Progress: 10%")
+
+    with rasterio.open(input_raster) as raster:
+        typer.echo("Progress: 25%")
+        out_image, out_meta = frost_filter(raster=raster, size=size, damping_factor=damping_factor)
+    typer.echo("Progress: 75%")
+
+    with rasterio.open(output_raster, "w", **out_meta) as dest:
+        dest.write(out_image, 1)
+    typer.echo("Progress: 100%")
+
+    typer.echo(f"Frost filter applied, output raster written to {output_raster}.")
+
+
+# KUAN FILTER
+@app.command()
+def kuan_filter_cli(
+    input_raster: Annotated[Path, INPUT_FILE_OPTION],
+    output_raster: Annotated[Path, OUTPUT_FILE_OPTION],
+    size: int = 3,
+    n_looks: int = 1,
+):
+    """Apply a Kuan filter to the input raster."""
+    from eis_toolkit.raster_processing.filters.speckle import kuan_filter
+
+    typer.echo("Progress: 10%")
+
+    with rasterio.open(input_raster) as raster:
+        typer.echo("progress: 25%")
+        out_image, out_meta = kuan_filter(raster=raster, size=size, n_looks=n_looks)
+    typer.echo("Progress: 75%")
+
+    with rasterio.open(output_raster, "w", **out_meta) as dest:
+        dest.write(out_image, 1)
+    typer.echo("Progress: 100%")
+
+    typer.echo(f"Kuan filter applied, output raster written to {output_raster}.")
+
+
 # CHECK RASTER GRIDS
 @app.command()
 def check_raster_grids_cli(input_rasters: INPUT_FILES_ARGUMENT, same_extent: bool = False):
diff --git a/eis_toolkit/raster_processing/filters/speckle.py b/eis_toolkit/raster_processing/filters/speckle.py
index 387fd801..b1aa2779 100644
--- a/eis_toolkit/raster_processing/filters/speckle.py
+++ b/eis_toolkit/raster_processing/filters/speckle.py
@@ -179,7 +179,7 @@ def _gamma(window: np.ndarray, n_looks: int) -> np.ndarray:
 
 
 @beartype
-def _frost(window: np.ndarray, damping_factor: int) -> Number:
+def _frost(window: np.ndarray, damping_factor: Number) -> Number:
     """
     Calculate the weighted value for a Frost filter from a window of pixels.
 
@@ -381,8 +381,8 @@ def lee_additive_multiplicative_noise_filter(
 def lee_enhanced_filter(
     raster: rasterio.io.DatasetReader,
     size: int = 3,
-    n_looks: Number = 1,
-    damping_factor: Number = 1,
+    n_looks: int = 1,
+    damping_factor: Number = 1.0,
 ) -> tuple[np.ndarray, dict]:
     """
     Apply an enhanced Lee filter to the input raster.
@@ -431,7 +431,7 @@ def lee_enhanced_filter(
 def gamma_filter(
     raster: rasterio.io.DatasetReader,
     size: int = 3,
-    n_looks: Number = 1,
+    n_looks: int = 1,
 ) -> tuple[np.ndarray, dict]:
     """
     Apply a Gamma filter to the input raster.
@@ -476,7 +476,7 @@ def gamma_filter(
 def frost_filter(
     raster: rasterio.io.DatasetReader,
     size: int = 3,
-    damping_factor: Number = 1,
+    damping_factor: Number = 1.0,
 ) -> tuple[np.ndarray, dict]:
     """
     Apply a Frost filter to the input raster.
@@ -521,7 +521,7 @@ def frost_filter(
 def kuan_filter(
     raster: rasterio.io.DatasetReader,
     size: int = 3,
-    n_looks: Number = 1,
+    n_looks: int = 1,
 ) -> tuple[np.ndarray, dict]:
     """
     Apply a Kuan filter to the input raster.