Testing with International Standard Test Image Lena

In this paper, we conducted testing using the international standard Lena test image. For color bias correction, we implemented two distinct approaches: the white balance method and the maximum color value balance method. These techniques represent fundamental image processing algorithms commonly employed across various applications. The white balance algorithm typically involves calculating color temperature adjustments by identifying neutral gray regions or using statistical methods to normalize color channels. The maximum color value balance method works by scaling each color channel based on its maximum intensity value to achieve color balance. These methods find practical implementation in numerous domains, such as enhancing image quality in digital cameras and mobile phone cameras, or improving medical imaging and satellite imagery. During our testing phase, we documented the advantages and limitations of each method while analyzing their performance characteristics. Our implementation included code for channel-wise normalization and histogram analysis to evaluate correction effectiveness. Our findings indicate that both methods demonstrate effective color bias correction under different scenarios. However, specific applications may favor one method over another depending on contextual factors. Therefore, when selecting a color correction approach, multiple considerations must be evaluated including image type, application requirements, and performance specifications. The implementation typically involves color space conversion, channel separation, and statistical analysis of pixel distributions to determine optimal correction parameters.