diff --git a/src/vstarstack/library/calibration/flat.py b/src/vstarstack/library/calibration/flat.py
index a75cbab4..41e8ed89 100644
--- a/src/vstarstack/library/calibration/flat.py
+++ b/src/vstarstack/library/calibration/flat.py
@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2022 Vladislav Tsendrovskii
+# Copyright (c) 2022-2024 Vladislav Tsendrovskii
 #
 # This program is free software: you can redistribute it and/or modify
 # it under the terms of the GNU General Public License as published by
@@ -12,8 +12,11 @@
 # along with this program. If not, see <https://www.gnu.org/licenses/>.
 #
 
+import math
+from typing import Tuple
 import cv2
 import numpy as np
+import scipy.signal
 
 import vstarstack.library.common
 import vstarstack.library.data
@@ -94,3 +97,109 @@ def prepare_flat_sky(images : vstarstack.library.common.IImageSource,
             continue
         flat.remove_channel(channel)
     return flat
+
+
+def get_quarters(image : np.ndarray, x : int, y : int) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+    """Get quarters of the image"""
+    w = image.shape[1]
+    h = image.shape[0]
+    left_top = image[0:y,0:x]
+    left_bottom = image[y:h,0:x]
+    right_top = image[0:y,x:w]
+    right_bottom = image[y:h,x:w]
+    left_top = left_top[::-1,::-1]
+    left_bottom = left_bottom[:,::-1]
+    right_top = right_top[::-1,:]
+    minw = min(left_top.shape[1], left_bottom.shape[1], right_top.shape[1], right_bottom.shape[1])
+    minh = min(left_top.shape[0], left_bottom.shape[0], right_top.shape[0], right_bottom.shape[0])
+    left_top = left_top[0:minh,0:minw]
+    left_bottom = left_bottom[0:minh,0:minw]
+    right_top = right_top[0:minh,0:minw]
+    right_bottom = right_bottom[0:minh,0:minw]
+    return left_top, left_bottom, right_top, right_bottom
+
+def correlate_images(im1 : np.ndarray, im2 : np.ndarray) -> float:
+    """Find correlation coefficient between 2 images"""
+    im1 = im1 - np.mean(im1)
+    im2 = im2 - np.mean(im2)
+    top = np.sum(im1*im2)
+    bottom1 = np.sum(im1**2)
+    bottom2 = np.sum(im2**2)
+    return top / math.sqrt(bottom1 * bottom2)
+
+def approximate_flat(image : np.ndarray) -> Tuple[int, int, float, float, float]:
+    """Find smooth polynomial approximation of flat"""
+    # We need to find center of vignetting and it's parameters
+    # We fill find them with gradient descend
+    w = image.shape[1]
+    h = image.shape[0]
+
+    x0, y0 = int(w/2), int(h/2)
+    corrmax = -1
+
+    centers = [(int(h/2), int(w/2))]
+    for y in range(int(h/3),int(2*h/3)+1):
+        for x in range(int(w/3),int(2*w/3)+1):
+            centers.append((y,x))
+
+    image = np.clip(image, 0, None)
+    for y,x in centers:
+        p1,p2,p3,p4 = get_quarters(image, x, y)
+        c12 = correlate_images(p1, p2)
+        c13 = correlate_images(p1, p3)
+        c14 = correlate_images(p1, p4)
+        c23 = correlate_images(p2, p3)
+        c24 = correlate_images(p2, p4)
+        c34 = correlate_images(p3, p3)
+        corr = sum([c12, c13, c14, c23, c24, c34])/6
+        if corr > corrmax:
+            corrmax = corr
+            x0,y0 = x,y
+
+    p1,p2,p3,p4 = get_quarters(image, x0, y0)
+    ww = p1.shape[1]
+    hh = p1.shape[0]
+    
+    sw = int(ww/16)
+    sh = int(hh/16)
+
+    val0 = np.average([np.average(item[:sh,:sw]) for item in [p1,p2,p3,p4]])
+    val_x = np.average([np.average(item[:sh,ww-sw:ww]) for item in [p1,p2,p3,p4]])/val0
+    val_y = np.average([np.average(item[hh-sh:hh,:sw]) for item in [p1,p2,p3,p4]])/val0
+
+    k_x = (1-val_x)/ww**2
+    k_y = (1-val_y)/hh**2
+    return x0, y0, val0, k_x, k_y
+
+def generate_flat(w : int, h : int, x0 : int, y0 : int, val0 : float, k_x : float, k_y : float) -> np.ndarray:
+    """Generate flat from parameters"""
+    x, y = np.meshgrid(np.arange(0,w),np.arange(0,h))
+    approx = val0*(1-k_x*(x-x0)**2-k_y*(y-y0)**2)
+    return approx
+
+def detect_spots(image : np.ndarray, approximated : np.ndarray) -> np.ndarray:
+    """Detect spots on original flat and append them to approximated flat"""
+    return image
+
+def approximate_flat_image(flat : vstarstack.library.data.DataFrame) -> vstarstack.library.data.DataFrame:
+    """Approximate flat"""
+    for channel in flat.get_channels():
+        if not flat.get_channel_option(channel, "signal"):
+            continue
+        layer,opts = flat.get_channel(channel)
+        layer = layer.astype(np.float64)
+        h,w = layer.shape
+        minv = min(w,h)
+        k = 128/minv
+        h = int(h*k)
+        w = int(w*k)
+        layer_small = cv2.resize(layer, (w,h), interpolation=cv2.INTER_LINEAR)
+        x0, y0, val0, kx, ky = approximate_flat(layer_small)
+        x0 = x0 / k
+        y0 = y0 / k
+        kx = kx * k**2
+        ky = ky * k**2
+        layer_approximated = generate_flat(layer.shape[1], layer.shape[0], x0, y0, val0, kx, ky)
+        layer_approximated = detect_spots(layer, layer_approximated)
+        flat.replace_channel(layer_approximated, channel, **opts)
+    return flat
diff --git a/src/vstarstack/tool/calibration.py b/src/vstarstack/tool/calibration.py
index 52875bb1..e44af890 100644
--- a/src/vstarstack/tool/calibration.py
+++ b/src/vstarstack/tool/calibration.py
@@ -116,6 +116,14 @@ def _process_build_flat_sky(_project : vstarstack.tool.cfg.Project,
     vstarstack.tool.common.check_dir_exists(flat_fname)
     flat.store(flat_fname)
 
+def _process_approximate_flat(_project : vstarstack.tool.cfg.Project,
+                              argv : list[str]):
+    input_fname = argv[0]
+    output_fname = argv[1]
+    df = vstarstack.library.data.DataFrame.load(input_fname)
+    df = vstarstack.library.calibration.flat.approximate_flat_image(df)
+    df.store(output_fname)
+
 commands = {
     "flatten": (_process_flatten,
                 "Flatten image",
@@ -131,5 +139,8 @@ def _process_build_flat_sky(_project : vstarstack.tool.cfg.Project,
                            "flats/ flat.zip"),
     "build-flat-sky" : (_process_build_flat_sky,
                            "Create flat image - use sky images",
-                           "flats/ flat.zip")
+                           "flats/ flat.zip"),
+    "approximate-flat" : (_process_approximate_flat,
+                          "Approximate flat with polynomic function",
+                          "original_flat.zip result_flat.zip")
 }
diff --git a/tests/flat/flat.jpg b/tests/flat/flat.jpg
new file mode 100644
index 00000000..f51fe396
Binary files /dev/null and b/tests/flat/flat.jpg differ
diff --git a/tests/test_flat_approximation.py b/tests/test_flat_approximation.py
new file mode 100644
index 00000000..65cdd298
--- /dev/null
+++ b/tests/test_flat_approximation.py
@@ -0,0 +1,31 @@
+#
+# Copyright (c) 2024 Vladislav Tsendrovskii
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, version 3 of the License.
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+# See the GNU General Public License for more details.
+# You should have received a copy of the GNU General Public License
+# along with this program. If not, see <https://www.gnu.org/licenses/>.
+#
+import vstarstack.library.calibration.flat
+import vstarstack.library.loaders.classic
+import os
+import numpy as np
+
+dir_path = os.path.dirname(os.path.realpath(__file__))
+
+def test_flat():
+    dfg = vstarstack.library.loaders.classic.readjpeg(os.path.join(dir_path, 'flat/flat.jpg'))
+    df = next(dfg)
+    flat,_ = df.get_channel('L')
+    approx = vstarstack.library.calibration.flat.approximate_flat_image(df.copy())
+    flat_approx,_ = approx.get_channel('L')
+    assert flat_approx.shape == flat.shape
+    rel = flat_approx / flat
+    assert np.average(rel) < 1.05
+    assert np.average(rel) > 0.95
+    assert np.std(rel) < 0.05