Testing with International Standard Test Image Lena

The Lena image is one of the widely used benchmark images in the field of image processing, commonly employed to evaluate the effectiveness of algorithms. In color-related processing tasks, color bias correction is a crucial step aimed at eliminating color deviations in images caused by lighting or equipment factors.

When performing color bias correction on the Lena image, commonly used methods include White Balance and Maximum Color Value Balance:

White Balance Method: This approach assumes the presence of white or neutral gray areas in the image. By adjusting the color channels in these regions to achieve balance, it corrects the overall color distribution of the image. This method is suitable for scenarios where the light source color temperature is known or when the image contains clear white reference areas. In code implementation, this typically involves selecting reference regions and scaling RGB channels using ratios like R_scale = R_max/R_ref, G_scale = G_max/G_ref, B_scale = B_max/B_ref.

Maximum Color Value Balance Method: This technique normalizes based on the maximum values of each color channel in the image. For example, it takes the maximum values of RGB channels separately and scales other pixel values using these as benchmarks, making the dynamic range of each channel more consistent. This method is suitable for situations requiring quick color bias correction without clear white references. Implementation typically involves: max_R = max(image[:,:,0]), max_G = max(image[:,:,1]), max_B = max(image[:,:,2]), followed by channel-wise normalization.

Both methods have their advantages and disadvantages: the White Balance method depends on the accuracy of reference regions, while the Maximum Color Value method may be affected by extreme bright colors (such as highlights) in the image. Through comparative testing with the Lena image, the correction effects of different methods can be visually analyzed, providing references for practical applications.

(Note: In actual testing, results should be validated using objective metrics such as color difference ΔE or subjective visual assessments.)