## 2 Related Works  
There has been considerable research on uniformly correcting irregular colors in remote sensing images. These studies have primarily focused on Wallis filters, histogram matching, color transfer, and more recently, GAN-based methods.  
In Wallis filter research, [PERSON] et al. [18] proposed optimizing color, luminance, and contrast consistency among multiple remote sensing images by processing the chroma and luminance channels in the YCbCr color space to minimize color differences and used the Wallis transform to optimize luminance and contrast. [PERSON] et al. [19] proposed an improved Wallis Dodging method suitable for large-scale super-resolution reconstructed remote sensing images. The algorithm calculates the Wallis filter parameters for each image in a weighted manner and uses a weighted iterative method between adjacent images to reduce spatial error propagation, resulting in a more precise adjustment of color consistency across large-scale images. [PERSON] et al. [20] proposed using a homomorphic filter along with the Wallis transform to address irregular brightness distribution and color differences. The homomorphic filter adjusts brightness imbalances within the image to resolve the hotspot problem, while the Wallis transform reduces color differences between images to achieve color consistency. A template-based filtering method for seam removal was also employed to address irregularities.  
In histogram research, [PERSON] et al. [21] proposed a 3D histogram transformation to more precisely maintain color consistency in images. This method overcomes the limitations of existing 2D histogram transformation and enables high-accuracy color conversion even for images with complex color structures. [PERSON] et al. [22] proposed a method of individually matching histograms of color, brightness, and saturation to naturally transfer color styles between two images.  
In color transfer research, [PERSON] et al. [23] proposed matching the color distributions by adjusting the mean and standard deviation between the source and target images in the Lab color space, demonstrating that this can produce natural results while maintaining color consistency. [PERSON] et al. [24] performed color transfer by minimizing the difference in the probability density functions between two images in multidimensional space, resulting in more natural and consistent color transfer compared to conventional methods.  
In GAN-based research, [PERSON] et al. [25] proposed a color correction technique using GANs to solve the color distortion problem occurring in underwater environments. The proposed method enables more natural and consistent color restoration, significantly improving the accuracy of color correction and object detection in underwater environments. [PERSON] et al. [26] proposed a method to control the color of GAN-generated images using color histograms, which facilitates natural color transfer. Additionally, a new model called ReHistoGAN can realign the colors of an actual image to match the target color histogram.  
These related works have presented various approaches to solve the problem of color consistency in remote sensing images. While Wallis filters, histograms, and color transfer methods each have their own advantages, they have also shown limitations in dealing with complex color structures and large-scale images. Recent GAN-based studies have overcome some of these limitations, but challenges remain in learning stability and producing consistent results. In this study, a new approach using a WGAN-GP model is proposed to overcome the limitations of these previous studies.