generated from Hazel/python-project
changed deconvolution algorythm
This commit is contained in:
Hazel Noack
parent
edd8096030
commit
37a5da37b0
@ -9,11 +9,24 @@ from PyQt5.QtCore import Qt
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from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
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from matplotlib.figure import Figure
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import scipy.signal
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from scipy.signal import convolve2d
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import os
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os.environ.pop("QT_QPA_PLATFORM_PLUGIN_PATH", None)
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def generate_box_kernel(size):
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return np.ones((size, size), dtype=np.float32) / (size * size)
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def generate_disk_kernel(radius):
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size = 2 * radius + 1
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y, x = np.ogrid[-radius:radius+1, -radius:radius+1]
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mask = x**2 + y**2 <= radius**2
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kernel = np.zeros((size, size), dtype=np.float32)
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kernel[mask] = 1
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kernel /= kernel.sum()
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return kernel
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def generate_kernel(radius, sigma=None):
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"""
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Generate a 2D Gaussian kernel with a given radius.
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@ -43,6 +56,58 @@ def generate_kernel(radius, sigma=None):
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return kernel
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def wiener_deconvolution(blurred, kernel, K=0.1):
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"""
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Perform Wiener deconvolution on a 2D image.
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Parameters:
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- blurred: 2D numpy array (blurred image)
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- kernel: 2D numpy array (PSF / blur kernel)
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- K: float, estimated noise-to-signal ratio
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Returns:
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- deconvolved: 2D numpy array (deblurred image)
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"""
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# Pad kernel to image size
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kernel /= np.sum(kernel)
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pad = [(0, blurred.shape[0] - kernel.shape[0]),
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(0, blurred.shape[1] - kernel.shape[1])]
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kernel_padded = np.pad(kernel, pad, 'constant')
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# FFT of image and kernel
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H = np.fft.fft2(kernel_padded)
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G = np.fft.fft2(blurred)
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# Avoid division by zero
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H_conj = np.conj(H)
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denominator = H_conj * H + K
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F_hat = H_conj / denominator * G
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# Inverse FFT to get result
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deconvolved = np.fft.ifft2(F_hat)
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deconvolved = np.abs(deconvolved)
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deconvolved = np.clip(deconvolved, 0, 255)
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return deconvolved.astype(np.uint8)
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def richardson_lucy(image, psf, iterations=30, clip=True):
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image = image.astype(np.float32) + 1e-6
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psf = psf / psf.sum()
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estimate = np.full(image.shape, 0.5, dtype=np.float32)
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psf_mirror = psf[::-1, ::-1]
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for _ in range(iterations):
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conv = convolve2d(estimate, psf, mode='same', boundary='wrap')
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relative_blur = image / (conv + 1e-6)
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estimate *= convolve2d(relative_blur, psf_mirror, mode='same', boundary='wrap')
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if clip:
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estimate = np.clip(estimate, 0, 255)
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return estimate
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class KernelVisualizer(QWidget):
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def __init__(self, image_path=None):
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super().__init__()
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@ -53,15 +118,15 @@ class KernelVisualizer(QWidget):
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self.load_button.clicked.connect(self.load_image)
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self.radius_slider = QSlider(Qt.Horizontal)
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self.radius_slider.setRange(1, 30)
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self.radius_slider.setRange(1, 100)
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self.radius_slider.setValue(5)
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self.sigma_slider = QSlider(Qt.Horizontal)
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self.sigma_slider.setRange(1, 100)
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self.sigma_slider.setRange(1, 300)
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self.sigma_slider.setValue(15)
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self.radius_slider.valueChanged.connect(self.update_visualization)
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self.sigma_slider.valueChanged.connect(self.update_visualization)
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# self.radius_slider.valueChanged.connect(self.update_visualization)
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# self.sigma_slider.valueChanged.connect(self.update_visualization)
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self.kernel_fig = Figure(figsize=(3, 3))
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self.kernel_canvas = FigureCanvas(self.kernel_fig)
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@ -69,15 +134,27 @@ class KernelVisualizer(QWidget):
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self.image_fig = Figure(figsize=(6, 3))
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self.image_canvas = FigureCanvas(self.image_fig)
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self.iter_slider = QSlider(Qt.Horizontal)
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self.iter_slider.setRange(1, 50)
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self.iter_slider.setValue(10)
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self.apply_button = QPushButton("Do Deconvolution.")
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self.apply_button.clicked.connect(self.update_visualization)
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layout = QVBoxLayout()
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layout.addWidget(self.load_button)
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sliders_layout = QGridLayout()
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sliders_layout.addWidget(QLabel("Radius:"), 0, 0)
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sliders_layout.addWidget(self.radius_slider, 0, 1)
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sliders_layout.addWidget(QLabel("Sigma:"), 1, 0)
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sliders_layout.addWidget(self.sigma_slider, 1, 1)
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sliders_layout.addWidget(QLabel("Iterations:"), 2, 0)
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sliders_layout.addWidget(self.iter_slider, 2, 1)
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sliders_layout.addWidget(self.apply_button, 3, 1)
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layout.addLayout(sliders_layout)
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layout.addWidget(QLabel("Kernel Visualization:"))
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layout.addWidget(self.kernel_canvas)
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@ -102,9 +179,20 @@ class KernelVisualizer(QWidget):
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self.image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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self.update_visualization()
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def load_image(self, image_path=None):
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if not image_path:
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fname, _ = QFileDialog.getOpenFileName(self, "Open Image", "", "Images (*.png *.jpg *.bmp *.jpeg)")
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image_path = fname
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if image_path:
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img = cv2.imread(image_path)
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if img is not None:
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self.image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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self.update_visualization()
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def update_visualization(self):
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radius = self.radius_slider.value()
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sigma = self.sigma_slider.value() / 10.0
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sigma = self.sigma_slider.value() / 10.0 * (radius / 3)
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kernel = generate_kernel(radius, sigma)
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# Kernel Visualization
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@ -116,34 +204,31 @@ class KernelVisualizer(QWidget):
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self.kernel_canvas.draw()
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if self.image is not None:
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kernel_size = 2 * radius + 1
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radius = self.radius_slider.value()
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sigma = self.sigma_slider.value() / 10.0
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iterations = self.iter_slider.value()
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# Apply row-by-row deconvolution
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deconvolved_image = np.zeros_like(self.image)
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kernel = generate_kernel(radius, sigma)
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blurred = cv2.filter2D(self.image, -1, kernel)
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# Perform row-wise deconvolution with padded kernel
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for i in range(self.image.shape[0]):
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padded_kernel = np.pad(kernel, ((0, self.image.shape[1] - kernel_size), (0, 0)), mode='constant')
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deconvolved_image[i, :], _ = scipy.signal.deconvolve(self.image[i, :], padded_kernel[i, :])
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# Perform column-wise deconvolution with padded kernel
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for j in range(self.image.shape[1]):
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padded_kernel = np.pad(kernel, ((0, self.image.shape[0] - kernel_size), (0, 0)), mode='constant')
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deconvolved_image[:, j], _ = scipy.signal.deconvolve(self.image[:, j], padded_kernel[:, j])
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deconvolved_image = np.clip(deconvolved_image, 0, 255).astype(np.uint8) # Ensure valid range
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deconvolved = richardson_lucy(blurred, kernel, iterations=iterations)
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self.image_fig.clear()
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ax1 = self.image_fig.add_subplot(121)
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ax1 = self.image_fig.add_subplot(131)
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ax1.imshow(self.image, cmap='gray')
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ax1.set_title("Original")
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ax1.axis('off')
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ax2 = self.image_fig.add_subplot(122)
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ax2.imshow(deconvolved_image, cmap='gray')
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ax2.set_title("Deconvolved")
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ax2 = self.image_fig.add_subplot(132)
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ax2.imshow(blurred, cmap='gray')
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ax2.set_title("Blurred")
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ax2.axis('off')
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ax3 = self.image_fig.add_subplot(133)
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ax3.imshow(deconvolved, cmap='gray')
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ax3.set_title(f"Deconvolved (RL, {iterations} iter)")
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ax3.axis('off')
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self.image_canvas.draw()
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else:
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self.image_fig.clear()
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