diff --git a/basic/image_smooth.py b/basic/image_smooth.py
index 02e5f83..4ce7b29 100644
--- a/basic/image_smooth.py
+++ b/basic/image_smooth.py
@@ -1,336 +1,326 @@
-import tkinter as tk
-from tkinter import filedialog, messagebox
-from tkinter import Toplevel
-from PIL import Image, ImageTk
-import numpy as np
-import cv2
-import os
-
-img_path = ""  # 全局变量，用于存储图像路径
-src = None  # 全局变量，用于存储已选择的图像
-img_label = None  # 全局变量，用于存储显示选择的图片的标签
-edge = None
-
-FreqsmoWin = None
-AirsmoWin  = None
-
-def select_image(root):
-    global img_path, src, img_label, edge
-
-    img_path = filedialog.askopenfilename(filetypes=[("Image files", "*.jpg;*.png;*.jpeg;*.bmp")])
-    if img_path:
-        # 确保路径中的反斜杠正确处理，并使用 UTF-8 编码处理中文路径
-        img_path_fixed = os.path.normpath(img_path)
-
-        # 图像输入
-        src_temp = cv2.imdecode(np.fromfile(img_path_fixed, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
-        if src_temp is None:
-            messagebox.showerror("错误", "无法读取图片，请选择有效的图片路径")
-            return
-        src = cv2.cvtColor(src_temp, cv2.COLOR_BGR2RGB)
-
-        # 检查 img_label 是否存在且有效
-        if img_label is None or not img_label.winfo_exists():
-            img_label = tk.Label(root)
-            img_label.pack(side=tk.TOP, pady=10)
-
-        img = Image.open(img_path)
-        img.thumbnail((160, 160))
-        img_tk = ImageTk.PhotoImage(img)
-        img_label.configure(image=img_tk)
-        img_label.image = img_tk
-
-        # 定义 edge 变量为 PIL.Image 对象，以便稍后保存
-        edge = Image.fromarray(src)
-    else:
-        messagebox.showerror("错误", "没有选择图片路径")
-
-def show_selected_image(root):
-    global img_label
-    img_label = tk.Label(root)
-    img_label.pack(side=tk.TOP, pady=10)
-    img = Image.open(img_path)
-    img.thumbnail((160, 160))
-    img_tk = ImageTk.PhotoImage(img)
-    img_label.configure(image=img_tk)
-    img_label.image = img_tk
-
-def changeSize(event, img, LabelPic):
-    img_aspect = img.shape[1] / img.shape[0]
-    new_aspect = event.width / event.height
-
-    if new_aspect > img_aspect:
-        new_width = int(event.height * img_aspect)
-        new_height = event.height
-    else:
-        new_width = event.width
-        new_height = int(event.width / img_aspect)
-
-    resized_image = cv2.resize(img, (new_width, new_height))
-    image1 = ImageTk.PhotoImage(Image.fromarray(resized_image))
-    LabelPic.image = image1
-    LabelPic['image'] = image1
-
-def savefile():
-    global edge
-
-    filename = filedialog.asksaveasfilename(defaultextension=".jpg", filetypes=[("JPEG files", "*.jpg"), ("PNG files", "*.png"), ("BMP files", "*.bmp")])
-    if not filename:
-        return
-    # 确保 edge 变量已定义
-    if edge is not None:
-        try:
-            edge.save(filename)
-            messagebox.showinfo("保存成功", "图片保存成功！")
-        except Exception as e:
-            messagebox.showerror("保存失败", f"无法保存图片: {e}")
-    else:
-        messagebox.showerror("保存失败", "没有图像可保存")
-
-#频域平滑
-def freq_smo(root):
-    global src, FreqsmoWin, edge
-
-    # 判断是否已经选取图片
-    if src is None:
-        messagebox.showerror("错误", "没有选择图片！")
-        return
-
-    # 理想低通滤波器
-    def Ideal_LowPassFilter(rows, cols, crow, ccol, D0=20):
-        # 创建一个与输入图像大小相同的空白图像
-        Ideal_LowPass = np.zeros((rows, cols), dtype=np.uint8)
-        # 创建理想低通滤波器
-        for i in range(rows):
-            for j in range(cols):
-                x = i - crow
-                y = j - ccol
-                D = np.sqrt(x**2 + y**2)
-                if D <= D0:
-                    Ideal_LowPass[i, j] = 255
-        # 应用滤波器到频域表示
-        mask = Ideal_LowPass[:, :, np.newaxis]
-        fshift = dft_shift * mask
-        # 逆傅里叶变换以获得平滑后的图像
-        f_ishift = np.fft.ifftshift(fshift)
-        img_back = cv2.idft(f_ishift)
-        img_back = cv2.magnitude(img_back[:, :, 0], img_back[:, :, 1])
-        # 归一化图像到0-255
-        cv2.normalize(img_back, img_back, 0, 255, cv2.NORM_MINMAX)
-        img_back = np.uint8(img_back)
-
-        return img_back
-
-    # 布特沃斯低通滤波器
-    def ButterWorth_LowPassFilter(rows, cols, crow, ccol, D0=20, n=2):
-        # 创建一个与输入图像大小相同的空白图像
-        ButterWorth_LowPass = np.zeros((rows, cols), dtype=np.uint8)
-        # 创建巴特沃斯低通滤波器
-        for i in range(rows):
-            for j in range(cols):
-                x = i - crow
-                y = j - ccol
-                D = np.sqrt(x ** 2 + y ** 2)
-                ButterWorth_LowPass[i, j] = 255 / (1 + (D / D0) ** (2 * n))
-        # 应用滤波器到频域表示
-        mask = ButterWorth_LowPass[:, :, np.newaxis]
-        fshift = dft_shift * mask
-        # 逆傅里叶变换以获得平滑后的图像
-        f_ishift = np.fft.ifftshift(fshift)
-        img_back = cv2.idft(f_ishift)
-        img_back = cv2.magnitude(img_back[:, :, 0], img_back[:, :, 1])
-        # 归一化图像到0-255
-        cv2.normalize(img_back, img_back, 0, 255, cv2.NORM_MINMAX)
-        img_back = np.uint8(img_back)
-
-        return img_back
-
-    # 高斯低通滤波器
-    def Gauss_LowPassFilter(rows, cols, crow, ccol, D0=20):
-        # 创建一个与输入图像大小相同的空白图像
-        Gauss_LowPass = np.zeros((rows, cols), dtype=np.uint8)
-        # 创建高斯低通滤波器
-        for i in range(rows):
-            for j in range(cols):
-                x = i - crow
-                y = j - ccol
-                D = np.sqrt(x ** 2 + y ** 2)
-                Gauss_LowPass[i, j] = 255 * np.exp(-0.5 * (D ** 2) / (D0 ** 2))
-        # 应用滤波器到频域表示
-        mask = Gauss_LowPass[:, :, np.newaxis]
-        fshift = dft_shift * mask
-        # 逆傅里叶变换以获得平滑后的图像
-        f_ishift = np.fft.ifftshift(fshift)
-        img_back = cv2.idft(f_ishift)
-        img_back = cv2.magnitude(img_back[:, :, 0], img_back[:, :, 1])
-        # 归一化图像到0-255
-        cv2.normalize(img_back, img_back, 0, 255, cv2.NORM_MINMAX)
-        img_back = np.uint8(img_back)
-
-        return img_back
-
-    # 读取灰度图像
-    im = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
-
-    # 获取图像的频域表示
-    dft = cv2.dft(np.float32(im), flags=cv2.DFT_COMPLEX_OUTPUT)
-    dft_shift = np.fft.fftshift(dft)
-
-    # 获取图像的尺寸
-    rows, cols = im.shape
-    crow, ccol = rows // 2, cols // 2
-
-    # 理想低通滤波器
-    Ideal_LowPass = Ideal_LowPassFilter(rows, cols, crow, ccol)
-    # 巴特沃斯低通滤波器
-    ButterWorth_LowPass = ButterWorth_LowPassFilter(rows, cols, crow, ccol)
-    # 高斯低通滤波器
-    Gauss_LowPass = Gauss_LowPassFilter(rows, cols, crow, ccol)
-
-    combined = np.hstack((Ideal_LowPass, ButterWorth_LowPass, Gauss_LowPass))
-    # 更新 edge 变量
-    edge = Image.fromarray(combined)
-
-    # 创建Toplevel窗口
-    try:
-        FreqsmoWin.destroy()
-    except Exception as e:
-        print("NVM")
-    finally:
-        FreqsmoWin = Toplevel()
-        FreqsmoWin.attributes('-topmost', True)
-    FreqsmoWin.geometry("720x300")
-    FreqsmoWin.resizable(True, True)  # 可缩放
-    FreqsmoWin.title("频域平滑结果")
-
-    # 显示图像
-    LabelPic = tk.Label(FreqsmoWin, text="IMG", width=720, height=240)
-    image = ImageTk.PhotoImage(Image.fromarray(combined))
-    LabelPic.image = image
-    LabelPic['image'] = image
-
-    LabelPic.bind('<Configure>', lambda event: changeSize(event, combined, LabelPic))
-    LabelPic.pack(fill=tk.BOTH, expand=tk.YES)
-
-    # 添加保存按钮
-    btn_save = tk.Button(FreqsmoWin, text="保存", bg='#add8e6', fg='black', font=('Helvetica', 14), width=20,
-                         command=savefile)
-    btn_save.pack(pady=10)
-
-    return
-
-#空域平滑
-def air_smo(root):
-    global src, AirsmoWin, edge
-
-    # 判断是否已经选取图片
-    if src is None:
-        messagebox.showerror("错误", "没有选择图片！")
-        return
-
-    # 均值平滑滤波
-    def mean_filter(image, height, width):
-        # 创建空白图像以存储滤波结果
-        filtered_image = np.zeros((height - 2, width - 2), dtype=np.uint8)
-
-        # 执行3x3均值滤波
-        for i in range(1, height - 1):
-            for j in range(1, width - 1):
-                tmp = (int(image[i - 1, j - 1]) + int(image[i - 1, j]) + int(image[i - 1, j + 1]) +
-                       int(image[i, j - 1]) + int(image[i, j]) + int(image[i, j + 1]) +
-                       int(image[i + 1, j - 1]) + int(image[i + 1, j]) + int(image[i + 1, j + 1])) // 9
-                filtered_image[i - 1, j - 1] = tmp
-
-        return filtered_image
-
-    # 中值平滑滤波
-    def median_filter(image, height, width):
-        # 创建空白图像以存储滤波结果
-        filtered_image = np.zeros((height - 2, width - 2), dtype=np.uint8)
-
-        # 执行3x3中值滤波
-        for i in range(1, height - 1):
-            for j in range(1, width - 1):
-                # 取3x3邻域
-                region = [
-                    image[i - 1, j - 1], image[i - 1, j], image[i - 1, j + 1],
-                    image[i, j - 1], image[i, j], image[i, j + 1],
-                    image[i + 1, j - 1], image[i + 1, j], image[i + 1, j + 1]
-                ]
-                # 计算中值
-                filtered_image[i - 1, j - 1] = np.median(region)
-
-        return filtered_image
-
-    # 5x5 中值平滑滤波
-    def med_filter_5x5(image, height, width):
-        # 创建空白图像以存储滤波结果
-        filtered_image = np.zeros((height - 4, width - 4), dtype=np.uint8)
-
-        # 执行5x5中值滤波
-        for i in range(2, height - 2):
-            for j in range(2, width - 2):
-                # 取5x5邻域的所有值
-                neighbors = [
-                    image[i - 2, j - 2], image[i - 2, j - 1], image[i - 2, j], image[i - 2, j + 1], image[i - 2, j + 2],
-                    image[i - 1, j - 2], image[i - 1, j - 1], image[i - 1, j], image[i - 1, j + 1], image[i - 1, j + 2],
-                    image[i, j - 2], image[i, j - 1], image[i, j], image[i, j + 1], image[i, j + 2],
-                    image[i + 1, j - 2], image[i + 1, j - 1], image[i + 1, j], image[i + 1, j + 1], image[i + 1, j + 2],
-                    image[i + 2, j - 2], image[i + 2, j - 1], image[i + 2, j], image[i + 2, j + 1], image[i + 2, j + 2]
-                ]
-                # 计算中值
-                filtered_image[i - 2, j - 2] = np.median(neighbors)
-
-        return filtered_image
-
-    # 读取灰度图像
-    im = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
-
-    # 获取图像尺寸
-    height, width = im.shape
-
-    # 邻域平均
-    mean = mean_filter(im, height, width)
-    # 中值滤波3x3
-    median = median_filter(im, height, width)
-    # 中值滤波5x5
-    med = med_filter_5x5(im, height, width)
-
-    min_height = min(mean.shape[0], median.shape[0], med.shape[0])
-    min_width = min(mean.shape[1], median.shape[1], med.shape[1])
-
-    mean_cropped = mean[:min_height, :min_width]
-    median_cropped = median[:min_height, :min_width]
-    med_cropped = med[:min_height, :min_width]
-
-    combined = np.hstack((mean_cropped, median_cropped, med_cropped))
-    # 更新 edge 变量
-    edge = Image.fromarray(combined)
-
-    # 创建Toplevel窗口
-    try:
-        AirsmoWin.destroy()
-    except Exception as e:
-        print("NVM")
-    finally:
-        AirsmoWin = Toplevel()
-        AirsmoWin.attributes('-topmost', True)
-    AirsmoWin.geometry("720x300")
-    AirsmoWin.resizable(True, True)  # 可缩放
-    AirsmoWin.title("空域平滑结果")
-
-    # 显示图像
-    LabelPic = tk.Label(AirsmoWin, text="IMG", width=720, height=240)
-    image = ImageTk.PhotoImage(Image.fromarray(combined))
-    LabelPic.image = image
-    LabelPic['image'] = image
-
-    LabelPic.bind('<Configure>', lambda event: changeSize(event, combined, LabelPic))
-    LabelPic.pack(fill=tk.BOTH, expand=tk.YES)
-
-    # 添加保存按钮
-    btn_save = tk.Button(AirsmoWin, text="保存", bg='#add8e6', fg='black', font=('Helvetica', 14), width=20,
-                         command=savefile)
-    btn_save.pack(pady=10)
-
+import tkinter as tk
+from tkinter import filedialog, messagebox
+from tkinter import Toplevel
+from PIL import Image, ImageTk
+import numpy as np
+import cv2
+import os
+
+img_path = ""  # 全局变量，用于存储图像路径
+src = None  # 全局变量，用于存储已选择的图像
+img_label = None  # 全局变量，用于存储显示选择的图片的标签
+edge = None
+
+FreqsmoWin = None
+AirsmoWin  = None
+
+def select_image(root):
+    global img_path, src, img_label, edge
+
+    img_path = filedialog.askopenfilename(filetypes=[("Image files", "*.jpg;*.png;*.jpeg;*.bmp")])
+    if img_path:
+        # 确保路径中的反斜杠正确处理，并使用 UTF-8 编码处理中文路径
+        img_path_fixed = os.path.normpath(img_path)
+
+        # 图像输入
+        src_temp = cv2.imdecode(np.fromfile(img_path_fixed, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
+        if src_temp is None:
+            messagebox.showerror("错误", "无法读取图片，请选择有效的图片路径")
+            return
+        src = cv2.cvtColor(src_temp, cv2.COLOR_BGR2RGB)
+
+        # 检查 img_label 是否存在且有效
+        if img_label is None or not img_label.winfo_exists():
+            img_label = tk.Label(root)
+            img_label.pack(side=tk.TOP, pady=10)
+
+        img = Image.open(img_path)
+        img.thumbnail((160, 160))
+        img_tk = ImageTk.PhotoImage(img)
+        img_label.configure(image=img_tk)
+        img_label.image = img_tk
+
+        # 定义 edge 变量为 PIL.Image 对象，以便稍后保存
+        edge = Image.fromarray(src)
+    else:
+        messagebox.showerror("错误", "没有选择图片路径")
+
+def changeSize(event, img, LabelPic):
+    img_aspect = img.shape[1] / img.shape[0]
+    new_aspect = event.width / event.height
+
+    if new_aspect > img_aspect:
+        new_width = int(event.height * img_aspect)
+        new_height = event.height
+    else:
+        new_width = event.width
+        new_height = int(event.width / img_aspect)
+
+    resized_image = cv2.resize(img, (new_width, new_height))
+    image1 = ImageTk.PhotoImage(Image.fromarray(resized_image))
+    LabelPic.image = image1
+    LabelPic['image'] = image1
+
+def savefile():
+    global edge
+
+    filename = filedialog.asksaveasfilename(defaultextension=".jpg", filetypes=[("JPEG files", "*.jpg"), ("PNG files", "*.png"), ("BMP files", "*.bmp")])
+    if not filename:
+        return
+    # 确保 edge 变量已定义
+    if edge is not None:
+        try:
+            edge.save(filename)
+            messagebox.showinfo("保存成功", "图片保存成功！")
+        except Exception as e:
+            messagebox.showerror("保存失败", f"无法保存图片: {e}")
+    else:
+        messagebox.showerror("保存失败", "没有图像可保存")
+
+#频域平滑
+def freq_smo(root):
+    global src, FreqsmoWin, edge
+
+    # 判断是否已经选取图片
+    if src is None:
+        messagebox.showerror("错误", "没有选择图片！")
+        return
+
+    # 理想低通滤波器
+    def Ideal_LowPassFilter(rows, cols, crow, ccol, D0=20):
+        # 创建一个与输入图像大小相同的空白图像
+        Ideal_LowPass = np.zeros((rows, cols), dtype=np.uint8)
+        # 创建理想低通滤波器
+        for i in range(rows):
+            for j in range(cols):
+                x = i - crow
+                y = j - ccol
+                D = np.sqrt(x**2 + y**2)
+                if D <= D0:
+                    Ideal_LowPass[i, j] = 255
+        # 应用滤波器到频域表示
+        mask = Ideal_LowPass[:, :, np.newaxis]
+        fshift = dft_shift * mask
+        # 逆傅里叶变换以获得平滑后的图像
+        f_ishift = np.fft.ifftshift(fshift)
+        img_back = cv2.idft(f_ishift)
+        img_back = cv2.magnitude(img_back[:, :, 0], img_back[:, :, 1])
+        # 归一化图像到0-255
+        cv2.normalize(img_back, img_back, 0, 255, cv2.NORM_MINMAX)
+        img_back = np.uint8(img_back)
+
+        return img_back
+
+    # 布特沃斯低通滤波器
+    def ButterWorth_LowPassFilter(rows, cols, crow, ccol, D0=20, n=2):
+        # 创建一个与输入图像大小相同的空白图像
+        ButterWorth_LowPass = np.zeros((rows, cols), dtype=np.uint8)
+        # 创建巴特沃斯低通滤波器
+        for i in range(rows):
+            for j in range(cols):
+                x = i - crow
+                y = j - ccol
+                D = np.sqrt(x ** 2 + y ** 2)
+                ButterWorth_LowPass[i, j] = 255 / (1 + (D / D0) ** (2 * n))
+        # 应用滤波器到频域表示
+        mask = ButterWorth_LowPass[:, :, np.newaxis]
+        fshift = dft_shift * mask
+        # 逆傅里叶变换以获得平滑后的图像
+        f_ishift = np.fft.ifftshift(fshift)
+        img_back = cv2.idft(f_ishift)
+        img_back = cv2.magnitude(img_back[:, :, 0], img_back[:, :, 1])
+        # 归一化图像到0-255
+        cv2.normalize(img_back, img_back, 0, 255, cv2.NORM_MINMAX)
+        img_back = np.uint8(img_back)
+
+        return img_back
+
+    # 高斯低通滤波器
+    def Gauss_LowPassFilter(rows, cols, crow, ccol, D0=20):
+        # 创建一个与输入图像大小相同的空白图像
+        Gauss_LowPass = np.zeros((rows, cols), dtype=np.uint8)
+        # 创建高斯低通滤波器
+        for i in range(rows):
+            for j in range(cols):
+                x = i - crow
+                y = j - ccol
+                D = np.sqrt(x ** 2 + y ** 2)
+                Gauss_LowPass[i, j] = 255 * np.exp(-0.5 * (D ** 2) / (D0 ** 2))
+        # 应用滤波器到频域表示
+        mask = Gauss_LowPass[:, :, np.newaxis]
+        fshift = dft_shift * mask
+        # 逆傅里叶变换以获得平滑后的图像
+        f_ishift = np.fft.ifftshift(fshift)
+        img_back = cv2.idft(f_ishift)
+        img_back = cv2.magnitude(img_back[:, :, 0], img_back[:, :, 1])
+        # 归一化图像到0-255
+        cv2.normalize(img_back, img_back, 0, 255, cv2.NORM_MINMAX)
+        img_back = np.uint8(img_back)
+
+        return img_back
+
+    # 读取灰度图像
+    im = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
+
+    # 获取图像的频域表示
+    dft = cv2.dft(np.float32(im), flags=cv2.DFT_COMPLEX_OUTPUT)
+    dft_shift = np.fft.fftshift(dft)
+
+    # 获取图像的尺寸
+    rows, cols = im.shape
+    crow, ccol = rows // 2, cols // 2
+
+    # 理想低通滤波器
+    Ideal_LowPass = Ideal_LowPassFilter(rows, cols, crow, ccol)
+    # 巴特沃斯低通滤波器
+    ButterWorth_LowPass = ButterWorth_LowPassFilter(rows, cols, crow, ccol)
+    # 高斯低通滤波器
+    Gauss_LowPass = Gauss_LowPassFilter(rows, cols, crow, ccol)
+
+    combined = np.hstack((Ideal_LowPass, ButterWorth_LowPass, Gauss_LowPass))
+    # 更新 edge 变量
+    edge = Image.fromarray(combined)
+
+    # 创建Toplevel窗口
+    try:
+        FreqsmoWin.destroy()
+    except Exception as e:
+        print("NVM")
+    finally:
+        FreqsmoWin = Toplevel()
+        FreqsmoWin.attributes('-topmost', True)
+    FreqsmoWin.geometry("720x300")
+    FreqsmoWin.resizable(True, True)  # 可缩放
+    FreqsmoWin.title("频域平滑结果")
+
+    # 显示图像
+    LabelPic = tk.Label(FreqsmoWin, text="IMG", width=720, height=240)
+    image = ImageTk.PhotoImage(Image.fromarray(combined))
+    LabelPic.image = image
+    LabelPic['image'] = image
+
+    LabelPic.bind('<Configure>', lambda event: changeSize(event, combined, LabelPic))
+    LabelPic.pack(fill=tk.BOTH, expand=tk.YES)
+
+    # 添加保存按钮
+    btn_save = tk.Button(FreqsmoWin, text="保存", bg='#add8e6', fg='black', font=('Helvetica', 14), width=20,
+                         command=savefile)
+    btn_save.pack(pady=10)
+
+    return
+
+#空域平滑
+def air_smo(root):
+    global src, AirsmoWin, edge
+
+    # 判断是否已经选取图片
+    if src is None:
+        messagebox.showerror("错误", "没有选择图片！")
+        return
+
+    # 均值平滑滤波
+    def mean_filter(image, height, width):
+        # 创建空白图像以存储滤波结果
+        filtered_image = np.zeros((height - 2, width - 2), dtype=np.uint8)
+
+        # 执行3x3均值滤波
+        for i in range(1, height - 1):
+            for j in range(1, width - 1):
+                tmp = (int(image[i - 1, j - 1]) + int(image[i - 1, j]) + int(image[i - 1, j + 1]) +
+                       int(image[i, j - 1]) + int(image[i, j]) + int(image[i, j + 1]) +
+                       int(image[i + 1, j - 1]) + int(image[i + 1, j]) + int(image[i + 1, j + 1])) // 9
+                filtered_image[i - 1, j - 1] = tmp
+
+        return filtered_image
+
+    # 中值平滑滤波
+    def median_filter(image, height, width):
+        # 创建空白图像以存储滤波结果
+        filtered_image = np.zeros((height - 2, width - 2), dtype=np.uint8)
+
+        # 执行3x3中值滤波
+        for i in range(1, height - 1):
+            for j in range(1, width - 1):
+                # 取3x3邻域
+                region = [
+                    image[i - 1, j - 1], image[i - 1, j], image[i - 1, j + 1],
+                    image[i, j - 1], image[i, j], image[i, j + 1],
+                    image[i + 1, j - 1], image[i + 1, j], image[i + 1, j + 1]
+                ]
+                # 计算中值
+                filtered_image[i - 1, j - 1] = np.median(region)
+
+        return filtered_image
+
+    # 5x5 中值平滑滤波
+    def med_filter_5x5(image, height, width):
+        # 创建空白图像以存储滤波结果
+        filtered_image = np.zeros((height - 4, width - 4), dtype=np.uint8)
+
+        # 执行5x5中值滤波
+        for i in range(2, height - 2):
+            for j in range(2, width - 2):
+                # 取5x5邻域的所有值
+                neighbors = [
+                    image[i - 2, j - 2], image[i - 2, j - 1], image[i - 2, j], image[i - 2, j + 1], image[i - 2, j + 2],
+                    image[i - 1, j - 2], image[i - 1, j - 1], image[i - 1, j], image[i - 1, j + 1], image[i - 1, j + 2],
+                    image[i, j - 2], image[i, j - 1], image[i, j], image[i, j + 1], image[i, j + 2],
+                    image[i + 1, j - 2], image[i + 1, j - 1], image[i + 1, j], image[i + 1, j + 1], image[i + 1, j + 2],
+                    image[i + 2, j - 2], image[i + 2, j - 1], image[i + 2, j], image[i + 2, j + 1], image[i + 2, j + 2]
+                ]
+                # 计算中值
+                filtered_image[i - 2, j - 2] = np.median(neighbors)
+
+        return filtered_image
+
+    # 读取灰度图像
+    im = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
+
+    # 获取图像尺寸
+    height, width = im.shape
+
+    # 邻域平均
+    mean = mean_filter(im, height, width)
+    # 中值滤波3x3
+    median = median_filter(im, height, width)
+    # 中值滤波5x5
+    med = med_filter_5x5(im, height, width)
+
+    min_height = min(mean.shape[0], median.shape[0], med.shape[0])
+    min_width = min(mean.shape[1], median.shape[1], med.shape[1])
+
+    mean_cropped = mean[:min_height, :min_width]
+    median_cropped = median[:min_height, :min_width]
+    med_cropped = med[:min_height, :min_width]
+
+    combined = np.hstack((mean_cropped, median_cropped, med_cropped))
+    # 更新 edge 变量
+    edge = Image.fromarray(combined)
+
+    # 创建Toplevel窗口
+    try:
+        AirsmoWin.destroy()
+    except Exception as e:
+        print("NVM")
+    finally:
+        AirsmoWin = Toplevel()
+        AirsmoWin.attributes('-topmost', True)
+    AirsmoWin.geometry("720x300")
+    AirsmoWin.resizable(True, True)  # 可缩放
+    AirsmoWin.title("空域平滑结果")
+
+    # 显示图像
+    LabelPic = tk.Label(AirsmoWin, text="IMG", width=720, height=240)
+    image = ImageTk.PhotoImage(Image.fromarray(combined))
+    LabelPic.image = image
+    LabelPic['image'] = image
+
+    LabelPic.bind('<Configure>', lambda event: changeSize(event, combined, LabelPic))
+    LabelPic.pack(fill=tk.BOTH, expand=tk.YES)
+
+    # 添加保存按钮
+    btn_save = tk.Button(AirsmoWin, text="保存", bg='#add8e6', fg='black', font=('Helvetica', 14), width=20,
+                         command=savefile)
+    btn_save.pack(pady=10)
+
     return
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