我们可以从应用imflatfield（2-D图像平场校正）开始，然后应用降噪和锐化。
请注意，建议的解决方案不能保证改善条形码检测，也不能保证适用于所有图像。
建议阶段：

• 应用2-D图像平场校正。

我们可以使用我的following answer中的Python实现。

flat_img = imflatfield(img, 15)  # Apply imflatfield with relatively small sigma - smaller sigma means that the brightness of each filter is determined by smaller area around it.

• 使用非局部均值滤波器去除噪声，以减少“划痕”伪影。

注：使用非局部均值的想法来自following post。

denoised_flat_img = cv2.fastNlMeansDenoisingColored(flat_img, None, 3, 3, 7)

• 锐化非局部均值的结果-需要，因为非局部均值会使图像平滑得太多。

# https://stackoverflow.com/questions/4993082/how-can-i-sharpen-an-image-in-opencv
 blur = cv2.GaussianBlur(denoised_flat_img, (0, 0), 3)
 sharpened_flat_img = cv2.addWeighted(denoised_flat_img, 1.5, blur, -0.5, 0);

代码示例：

import cv2
import numpy as np

# https://stackoverflow.com/questions/61087996/imflatfield-matlab-for-python-use
def imflatfield(I, sigma):
    """Python equivalent imflatfield implementation
       I format must be BGR uint8"""
    A = I.astype(np.float32) / 255  # A = im2single(I);
    Ihsv = cv2.cvtColor(A, cv2.COLOR_BGR2HSV)  # Ihsv = rgb2hsv(A);
    A = Ihsv[:, :, 2]  # A = Ihsv(:,:,3);
    
    filterSize = int(2*np.ceil(2*sigma) + 1);  # filterSize = 2*ceil(2*sigma)+1;
    
    # shading = imgaussfilt(A, sigma, 'Padding', 'symmetric', 'FilterSize', filterSize); % Calculate shading
    shading = cv2.GaussianBlur(A, (filterSize, filterSize), sigma, borderType=cv2.BORDER_REFLECT)
    
    meanVal = np.mean(A)  # meanVal = mean(A(:),'omitnan')
    
    #% Limit minimum to 1e-6 instead of testing using isnan and isinf after division.
    shading = np.maximum(shading, 1e-6)  # shading = max(shading, 1e-6);
    
    B = A*meanVal / shading  # B = A*meanVal./shading;
    
    #% Put processed V channel back into HSV image, convert to RGB
    Ihsv[:, :, 2] = B  # Ihsv(:,:,3) = B;
    
    B = cv2.cvtColor(Ihsv, cv2.COLOR_HSV2BGR)  # B = hsv2rgb(Ihsv);
    
    B = np.round(np.clip(B*255, 0, 255)).astype(np.uint8)  # B = im2uint8(B);

    return B

img = cv2.imread('barcode.png')

# Apply imflatfield with relatively small sigma - smaller sigma means that the brightness of each filter is determined by smaller area around it. 
flat_img = imflatfield(img, 15)

# Remove noise using Non-local means filter
denoised_flat_img = cv2.fastNlMeansDenoisingColored(flat_img, None, 3, 3, 7)

# https://stackoverflow.com/questions/4993082/how-can-i-sharpen-an-image-in-opencv
# Sharpen the result of Non-local means
blur = cv2.GaussianBlur(denoised_flat_img, (0, 0), 3)
sharpened_flat_img = cv2.addWeighted(denoised_flat_img, 1.5, blur, -0.5, 0);

# Show results for testing
cv2.imshow('img', img)
cv2.imshow('flat_img', flat_img)
cv2.imshow('denoised_flat_img', denoised_flat_img)
cv2.imshow('sharpened_flat_img', sharpened_flat_img)
cv2.waitKey()
cv2.destroyAllWindows()

原始图像：

flat_img：

denoised_flat_img：

sharpened_flat_img：

注意事项：
最好的解决方案是在捕获图像时去除反射（如果可能的话）。
图像处理解决方案（在当前的上下文中）应该更像学术主题（对于解决“真实的问题”可能不是很实用）。