diff --git a/basic/noise_filter.py b/basic/noise_filter.py
new file mode 100644
index 0000000..2c6fde9
--- /dev/null
+++ b/basic/noise_filter.py
@@ -0,0 +1,303 @@
+import tkinter as tk
+from tkinter import filedialog, messagebox
+from tkinter import Toplevel
+from PIL import Image, ImageTk
+import numpy as np
+import math
+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
+X = None  # 用于存储第一张图像
+Y = None  # 用于存储第二张图像
+img_label = None  # 全局变量，用于存储显示选择的图片的标签
+
+GeomeanWin = None
+SortstatWin = None
+SelectWin = 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")
+
+#几何均值滤波
+def geo_mean_filter(root):
+    global src, GeomeanWin, 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
+
+    image = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
+
+    # 待输出的图片
+    output = np.zeros(image.shape, np.uint8)
+    # 遍历图像，进行均值滤波
+    for i in range(image.shape[0]):
+        for j in range(image.shape[1]):
+            # 计算均值,完成对图片src的几何均值滤波
+            ji = 1.0
+
+            # 遍历滤波器内的像素值
+            for k in range(-1, 2):
+                if 0 <= j + k < image.shape[1]:
+                    ji *= image[i, j + k]
+
+            # 滤波器的大小为1*3
+            geom_mean = math.pow(ji, 1 / 3.0)
+            output[i, j] = int(geom_mean)
+
+    # 更新 edge 变量
+    edge = Image.fromarray(output)
+
+    # 创建Toplevel窗口
+    try:
+        GeomeanWin.destroy()
+    except Exception:
+        print("NVM")
+    finally:
+        GeomeanWin = Toplevel()
+        GeomeanWin.attributes('-topmost', True)
+    GeomeanWin.geometry("720x300")
+    GeomeanWin.resizable(True, True)  # 可缩放
+    GeomeanWin.title("几何均值滤波结果")
+
+    # 显示图像
+    LabelPic = tk.Label(GeomeanWin, text="IMG", width=360, height=240)
+    image = ImageTk.PhotoImage(Image.fromarray(output))
+    LabelPic.image = image
+    LabelPic['image'] = image
+
+    LabelPic.bind('<Configure>', lambda event: changeSize(event, output, LabelPic))
+    LabelPic.pack(fill=tk.BOTH, expand=tk.YES)
+
+    # 添加保存按钮
+    btn_save = tk.Button(GeomeanWin, text="保存", bg='#add8e6', fg='black', font=('Helvetica', 14), width=20,
+                         command=savefile)
+    btn_save.pack(pady=10)
+
+    return
+
+#排序统计类滤波
+def sort_stat_filter(root):
+    global src, SortstatWin, combined, 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
+
+    image = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
+
+    # 待输出的图片
+    output_max = np.zeros(image.shape, np.uint8)
+    output_median = np.zeros(image.shape, np.uint8)
+    output_min = np.zeros(image.shape, np.uint8)
+
+    # 遍历图像
+    for i in range(image.shape[0]):
+        for j in range(image.shape[1]):
+            # 用于存储3x3邻域像素值
+            neighbors = []
+            for m in range(-1, 2):
+                for n in range(-1, 2):
+                    if 0 <= i + m < image.shape[0] and 0 <= j + n < image.shape[1]:
+                        neighbors.append(image[i + m, j + n])
+
+            # 最大值滤波
+            output_max[i, j] = max(neighbors)
+            # 中值滤波
+            output_median[i, j] = np.median(neighbors)
+            # 最小值滤波
+            output_min[i, j] = min(neighbors)
+
+            combined = np.hstack((output_max, output_median, output_min))
+
+    # 更新 edge 变量
+    edge = Image.fromarray(combined)
+
+    # 创建Toplevel窗口
+    try:
+        SortstatWin.destroy()
+    except Exception:
+        print("NVM")
+    finally:
+        SortstatWin = Toplevel()
+        SortstatWin.attributes('-topmost', True)
+    SortstatWin.geometry("720x300")
+    SortstatWin.resizable(True, True)  # 可缩放
+    SortstatWin.title("排序统计类滤波结果")
+
+    # 显示图像
+    LabelPic = tk.Label(SortstatWin, text="IMG", width=360, 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(SortstatWin, text="保存", bg='#add8e6', fg='black', font=('Helvetica', 14), width=20,
+                         command=savefile)
+    btn_save.pack(pady=10)
+
+    return
+
+#选择性滤波
+def selective_filter(root):
+    global src, SelectWin, combined, 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
+
+    image = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
+
+    # 待输出的图片
+    output_low_pass = np.zeros(image.shape, np.uint8)
+    output_high_pass = np.zeros(image.shape, np.uint8)
+    output_band_pass = np.zeros(image.shape, np.uint8)
+    output_band_stop = np.zeros(image.shape, np.uint8)
+
+    # 边界
+    low_pass_threshold = 100
+    high_pass_threshold = 100
+    band_pass_low_threshold = 100
+    band_pass_high_threshold = 200
+    band_stop_low_threshold = 100
+    band_stop_high_threshold = 200
+
+    # 遍历图像
+    for i in range(image.shape[0]):
+        for j in range(image.shape[1]):
+            pixel_value = image[i][j]
+
+            # 低通滤波器
+            if pixel_value <= low_pass_threshold:
+                output_low_pass[i][j] = pixel_value
+            else:
+                output_low_pass[i][j] = 0
+
+            # 高通滤波器
+            if pixel_value >= high_pass_threshold:
+                output_high_pass[i][j] = pixel_value
+            else:
+                output_high_pass[i][j] = 0
+
+            # 带通滤波器
+            if band_pass_low_threshold < pixel_value <= band_pass_high_threshold:
+                output_band_pass[i][j] = pixel_value
+            else:
+                output_band_pass[i][j] = 0
+
+            # 带阻滤波器
+            if band_stop_low_threshold < pixel_value <= band_stop_high_threshold:
+                output_band_stop[i][j] = 0
+            else:
+                output_band_stop[i][j] = pixel_value
+
+        combined = np.hstack((output_low_pass, output_high_pass, output_band_pass, output_band_stop))
+
+    # 更新 edge 变量
+    edge = Image.fromarray(combined)
+
+    # 创建Toplevel窗口
+    try:
+        SelectWin.destroy()
+    except Exception:
+        print("NVM")
+    finally:
+        SelectWin = Toplevel()
+        SelectWin.attributes('-topmost', True)
+    SelectWin.geometry("720x300")
+    SelectWin.resizable(True, True)  # 可缩放
+    SelectWin.title("选择性滤波结果")
+
+    # 显示图像
+    LabelPic = tk.Label(SelectWin, text="IMG", width=360, 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(SelectWin, text="保存", bg='#add8e6', fg='black', font=('Helvetica', 14), width=20,
+                         command=savefile)
+    btn_save.pack(pady=10)
+
+    return
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