diff --git a/transforms/images/apply-flatfield-plugin/pyproject.toml b/transforms/images/apply-flatfield-plugin/pyproject.toml
new file mode 100644
index 000000000..d4e72a227
--- /dev/null
+++ b/transforms/images/apply-flatfield-plugin/pyproject.toml
@@ -0,0 +1,38 @@
+[tool.poetry]
+name = "polus-plugins-transforms-images-apply-flatfield"
+version = "2.0.0-dev7"
+description = ""
+authors = [
+    "Nick Schaub <nicholas.schaub@nih.gov>",
+    "Najib Ishaq <najib.ishaq@nih.gov>"
+]
+readme = "README.md"
+packages = [{include = "polus", from = "src"}]
+
+[tool.poetry.dependencies]
+python = "^3.9"
+bfio = { version = "2.1.9", extras = ["all"] }
+filepattern = [
+    { version = "^2.0.0", platform = "linux" },
+    { version = "^2.0.0", platform = "win32" },
+    # { git = "https://github.com/PolusAI/filepattern", rev = "c07bf543c435cbc4cf264effd5a178868e9eaf19", platform = "darwin" },
+    { git = "https://github.com/JesseMckinzie/filepattern-1", rev = "c27cf04ba3a1946b87c0c43d5720ba394c340894", platform = "darwin" },
+]
+typer = { version = "^0.7.0", extras = ["all"] }
+numpy = "^1.24.3"
+tqdm = "^4.65.0"
+
+[tool.poetry.group.dev.dependencies]
+pytest = "^7.2.1"
+pytest-cov = "^4.0.0"
+pytest-sugar = "^0.9.6"
+pytest-xdist = "^3.2.0"
+pytest-benchmark = "^4.0.0"
+bump2version = "^1.0.1"
+pre-commit = "^3.0.4"
+black = "^23.1.0"
+ruff = "^0.0.265"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
diff --git a/transforms/images/apply-flatfield-plugin/src/polus/plugins/transforms/images/apply_flatfield/__init__.py b/transforms/images/apply-flatfield-plugin/src/polus/plugins/transforms/images/apply_flatfield/__init__.py
new file mode 100644
index 000000000..dd697c5a6
--- /dev/null
+++ b/transforms/images/apply-flatfield-plugin/src/polus/plugins/transforms/images/apply_flatfield/__init__.py
@@ -0,0 +1,160 @@
+"""Provides the apply_flatfield module."""
+
+import concurrent.futures
+import logging
+import operator
+import pathlib
+import sys
+import typing
+
+import bfio
+import numpy
+import tqdm
+from filepattern import FilePattern
+
+from . import utils
+
+logger = logging.getLogger(__name__)
+logger.setLevel(utils.POLUS_LOG)
+
+
+def apply(  # noqa: PLR0913
+    img_dir: pathlib.Path,
+    img_pattern: str,
+    ff_dir: pathlib.Path,
+    ff_pattern: str,
+    df_pattern: typing.Optional[str],
+    out_dir: pathlib.Path,
+) -> None:
+    """Run batch-wise flatfield correction on the image collection."""
+    img_fp = FilePattern(str(img_dir), img_pattern)
+    img_variables = img_fp.get_variables()
+
+    ff_fp = FilePattern(str(ff_dir), ff_pattern)
+    ff_variables = ff_fp.get_variables()
+
+    # check that ff_variables are a subset of img_variables
+    if set(ff_variables) - set(img_variables):
+        msg = (
+            f"Flatfield variables are not a subset of image variables: "
+            f"{ff_variables} - {img_variables}"
+        )
+        raise ValueError(msg)
+
+    if (df_pattern is None) or (not df_pattern):
+        df_fp = None
+    else:
+        df_fp = FilePattern(str(ff_dir), df_pattern)
+        df_variables = df_fp.get_variables()
+        if set(df_variables) != set(ff_variables):
+            msg = (
+                f"Flatfield and darkfield variables do not match: "
+                f"{ff_variables} != {df_variables}"
+            )
+            raise ValueError(msg)
+
+    for group, files in img_fp(group_by=ff_variables):
+        img_paths = [p for _, [p] in files]
+        variables = dict(group)
+
+        ff_path: pathlib.Path = ff_fp.get_matching(**variables)[0][1][0]
+
+        df_path = None if df_fp is None else df_fp.get_matching(**variables)[0][1][0]
+
+        _unshade_images(img_paths, out_dir, ff_path, df_path)
+
+
+def _unshade_images(
+    img_paths: list[pathlib.Path],
+    out_dir: pathlib.Path,
+    ff_path: pathlib.Path,
+    df_path: typing.Optional[pathlib.Path],
+) -> None:
+    """Remove the given flatfield components from all images and save outputs.
+
+    Args:
+        img_paths: list of paths to images to be processed
+        out_dir: directory to save the corrected images
+        ff_path: path to the flatfield image
+        df_path: path to the darkfield image
+    """
+    with bfio.BioReader(ff_path, max_workers=2) as bf:
+        ff_image = bf[:, :, :, 0, 0].squeeze()
+
+    if df_path is not None:
+        with bfio.BioReader(df_path, max_workers=2) as df:
+            df_image = df[:, :, :, 0, 0].squeeze()
+    else:
+        df_image = None
+
+    batch_indices = list(range(0, len(img_paths), 16))
+    if batch_indices[-1] != len(img_paths):
+        batch_indices.append(len(img_paths))
+
+    for i_start, i_end in tqdm.tqdm(
+        zip(batch_indices[:-1], batch_indices[1:]),
+        total=len(batch_indices) - 1,
+    ):
+        _unshade_batch(
+            img_paths[i_start:i_end],
+            out_dir,
+            ff_image,
+            df_image,
+        )
+
+
+def _unshade_batch(
+    batch_paths: list[pathlib.Path],
+    out_dir: pathlib.Path,
+    ff_image: numpy.ndarray,
+    df_image: typing.Optional[numpy.ndarray] = None,
+) -> None:
+    """Apply flatfield correction to a batch of images.
+
+    Args:
+        batch_paths: list of paths to images to be processed
+        out_dir: directory to save the corrected images
+        ff_image: component to be used for flatfield correction
+        df_image: component to be used for flatfield correction
+    """
+    # Load images
+    images = []
+    with concurrent.futures.ProcessPoolExecutor(
+        max_workers=utils.MAX_WORKERS,
+    ) as load_executor:
+        load_futures = []
+        for i, inp_path in enumerate(batch_paths):
+            load_futures.append(load_executor.submit(utils.load_img, inp_path, i))
+
+        for lf in tqdm.tqdm(
+            concurrent.futures.as_completed(load_futures),
+            total=len(load_futures),
+            desc="Loading batch",
+        ):
+            images.append(lf.result())
+
+    images = [img for _, img in sorted(images, key=operator.itemgetter(0))]
+    img_stack = numpy.stack(images, axis=0)
+
+    # Apply flatfield correction
+    if df_image is not None:
+        img_stack -= df_image
+
+    img_stack /= ff_image
+
+    # Save outputs
+    with concurrent.futures.ProcessPoolExecutor(
+        max_workers=utils.MAX_WORKERS,
+    ) as save_executor:
+        save_futures = []
+        for inp_path, img in zip(batch_paths, img_stack):
+            save_futures.append(
+                save_executor.submit(utils.save_img, inp_path, img, out_dir),
+            )
+
+        for sf in tqdm.tqdm(
+            concurrent.futures.as_completed(save_futures),
+            total=len(save_futures),
+            desc="Saving batch",
+        ):
+            sf.result()
diff --git a/transforms/images/apply-flatfield-plugin/tests/test_plugin.py b/transforms/images/apply-flatfield-plugin/tests/test_plugin.py
new file mode 100644
index 000000000..886773807
--- /dev/null
+++ b/transforms/images/apply-flatfield-plugin/tests/test_plugin.py
@@ -0,0 +1,151 @@
+"""Tests for the plugin."""
+
+import itertools
+import logging
+import pathlib
+import shutil
+import tempfile
+
+import bfio
+import numpy
+import pytest
+import typer.testing
+from polus.plugins.transforms.images.apply_flatfield import apply
+from polus.plugins.transforms.images.apply_flatfield.__main__ import app
+
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.DEBUG)
+
+
+def _make_random_image(
+    path: pathlib.Path,
+    rng: numpy.random.Generator,
+    size: int,
+) -> None:
+    with bfio.BioWriter(path) as writer:
+        writer.X = size
+        writer.Y = size
+        writer.dtype = numpy.float32
+
+        writer[:] = rng.random(size=(size, size), dtype=writer.dtype)
+
+
+FixtureReturnType = tuple[pathlib.Path, str, pathlib.Path, str]
+
+
+def gen_once(num_groups: int, img_size: int) -> FixtureReturnType:
+    """Generate a set of random images for testing."""
+
+    img_pattern = "img_x{x}_c{c}.ome.tif"
+    ff_pattern = "img_x(1-10)_c{c}"
+
+    img_dir = pathlib.Path(tempfile.mkdtemp(suffix="img_dir"))
+    ff_dir = pathlib.Path(tempfile.mkdtemp(suffix="ff_dir"))
+
+    rng = numpy.random.default_rng(42)
+
+    for i in range(num_groups):
+        ff_path = ff_dir.joinpath(f"{ff_pattern.format(c=i + 1)}_flatfield.ome.tif")
+        _make_random_image(ff_path, rng, img_size)
+
+        df_path = ff_dir.joinpath(f"{ff_pattern.format(c=i + 1)}_darkfield.ome.tif")
+        _make_random_image(df_path, rng, img_size)
+
+        for j in range(10):  # 10 images in each group
+            img_path = img_dir.joinpath(img_pattern.format(x=j + 1, c=i + 1))
+            _make_random_image(img_path, rng, img_size)
+
+    image_names = list(sorted(p.name for p in img_dir.iterdir()))
+    logger.debug(f"Generated {image_names} images in {img_dir}")
+
+    ff_names = list(sorted(p.name for p in ff_dir.iterdir()))
+    logger.debug(f"Generated {ff_names} flatfield images in {ff_dir}")
+
+    img_pattern = "img_x{x:d+}_c{c:d}.ome.tif"
+    ff_pattern = "img_x\\(1-10\\)_c{c:d}"
+    return img_dir, img_pattern, ff_dir, ff_pattern
+
+
+NUM_GROUPS = [2**i for i in range(3)]
+IMG_SIZES = [1024 * 2**i for i in range(3)]
+PARAMS = list(itertools.product(NUM_GROUPS, IMG_SIZES))
+IDS = [f"{num_groups}_{img_size}" for num_groups, img_size in PARAMS]
+
+
+@pytest.fixture(params=PARAMS, ids=IDS)
+def gen_images(request: pytest.FixtureRequest) -> FixtureReturnType:
+    """Generate a set of random images for testing."""
+    num_groups: int
+    img_size: int
+    num_groups, img_size = request.param
+    img_dir, img_pattern, ff_dir, ff_pattern = gen_once(num_groups, img_size)
+
+    yield img_dir, img_pattern, ff_dir, ff_pattern
+
+    # Cleanup
+    shutil.rmtree(img_dir)
+    shutil.rmtree(ff_dir)
+
+
+def test_estimate(gen_images: FixtureReturnType) -> None:
+    """Test the `estimate` function."""
+
+    img_dir, img_pattern, ff_dir, ff_pattern = gen_images
+    out_dir = pathlib.Path(tempfile.mkdtemp(suffix="out_dir"))
+
+    apply(
+        img_dir,
+        img_pattern,
+        ff_dir,
+        f"{ff_pattern}_flatfield.ome.tif",
+        f"{ff_pattern}_darkfield.ome.tif",
+        out_dir,
+    )
+
+    img_names = [p.name for p in img_dir.iterdir()]
+    out_names = [p.name for p in out_dir.iterdir()]
+
+    for name in img_names:
+        assert name in out_names, f"{name} not in {out_names}"
+
+    shutil.rmtree(out_dir)
+
+
+def test_cli() -> None:
+    """Test the CLI."""
+
+    img_dir, img_pattern, ff_dir, ff_pattern = gen_once(2, 2_048)
+    out_dir = pathlib.Path(tempfile.mkdtemp(suffix="out_dir"))
+
+    runner = typer.testing.CliRunner()
+
+    result = runner.invoke(
+        app,
+        [
+            "--imgDir",
+            str(img_dir),
+            "--imgPattern",
+            img_pattern,
+            "--ffDir",
+            str(ff_dir),
+            "--brightPattern",
+            f"{ff_pattern}_flatfield.ome.tif",
+            "--darkPattern",
+            f"{ff_pattern}_darkfield.ome.tif",
+            "--outDir",
+            str(out_dir),
+        ],
+    )
+
+    assert result.exit_code == 0, result.stdout
+
+    img_paths = set(p.name for p in img_dir.iterdir() if p.name.endswith(".ome.tif"))
+
+    out_names = set(p.name for p in out_dir.iterdir() if p.name.endswith(".ome.tif"))
+
+    assert img_paths == out_names, f"{(img_paths)} != {out_names}"
+
+    # Cleanup
+    shutil.rmtree(img_dir)
+    shutil.rmtree(ff_dir)
+    shutil.rmtree(out_dir)