feat: 2d deblurr

deblur/deblur_2d.py

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+import numpy as np
+from scipy.signal import convolve2d
+import cv2
+import matplotlib.pyplot as plt
+
+def show(img):
+    cv2.imshow('image',img.astype(np.uint8))
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+
+# Define 2D image and kernel
+image = cv2.imread('assets/omas.png', 0)
+image = cv2.resize(image, (200, 200), interpolation= cv2.INTER_LINEAR)
+
+kernel = np.array([
+    [1, 2, 1],
+    [2, 4, 2],
+    [1, 2, 1]
+], dtype=np.float32) 
+kernel /= kernel.sum()  # Normalize
+
+print(kernel)
+
+# Perform 2D convolution (blurring)
+blurred = convolve2d(image, kernel, mode="same", boundary="fill", fillvalue=0)
+
+show(image)
+show(blurred)
+
+print("Original image:\n", image)
+print("\nBlurred image:\n", blurred)
+
+print("\nBuilding linear system for deconvolution...")
+
+# Image size
+h, w = image.shape
+kh, kw = kernel.shape
+pad_h, pad_w = kh // 2, kw // 2
+
+# Build matrix A and vector b for Ax = b
+A = []
+b = []
+
+for y in range(h):
+    for x in range(w):
+        row = np.zeros((h, w), dtype=np.float32)
+
+        for ky in range(kh):
+            for kx in range(kw):
+                iy = y + ky - pad_h
+                ix = x + kx - pad_w
+                if 0 <= iy < h and 0 <= ix < w:
+                    row[iy, ix] += kernel[ky, kx]
+
+        A.append(row.flatten())
+        b.append(blurred[y, x])
+
+A = np.array(A)
+b = np.array(b)
+
+# Solve for the deblurred image
+deblurred_flat = np.linalg.solve(A, b)
+deblurred = deblurred_flat.reshape((h, w))
+
+print("\nDeblurred image:\n", np.round(deblurred, 2))
+
+show(deblurred)
+
+
+