如何改进Hough圆变换检测散乱点构成的圆

import cv2
import numpy as np

# 读取图像并转换为灰度
image = cv2.imread('circles.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# 预处理：高斯模糊和Canny边缘检测
blurred = cv2.GaussianBlur(gray, (9, 9), 2)
edges = cv2.Canny(blurred, 50, 150)

# 使用Hough圆变换检测圆
circles = cv2.HoughCircles(edges, cv2.HOUGH_GRADIENT, dp=1, minDist=50,
                           param1=100, param2=30, minRadius=10, maxRadius=100)

if circles is not None:
    circles = np.uint16(np.around(circles))
    for i in circles[0, :]:
        # 绘制圆心
        cv2.circle(image, (i[0], i[1]), i[2], (0, 255, 0), 2)
        # 绘制圆轮廓
        cv2.circle(image, (i[0], i[1]), 2, (0, 0, 255), 3)

# 显示结果
cv2.imshow('Detected Circles', image)
cv2.waitKey(0)
cv2.destroyAllWindows()