Color Correction and Naturalness Restoration for Multiple Images With Uneven Luminance

Abstract

When multiple synthetic aperture radar images are stitched together, the intensity disconnects between them can have a significant impact on the mosaic's quality. Many approaches focus on decreasing the intensity differences between images while ignoring the issue of image quality improvement. This study provides an algorithm for color correction and naturalness restoration for multiple images with uneven luminance in order to generate high-quality mosaics. To increase the illuminance component's naturalness, the image is first divided into illuminance and reflectance components, and the illuminance component is subjected to adaptive luminance improvement and contrast enhancement. Using a color consistency optimization approach, the intensity disparities between illuminance components are subsequently minimized. The reflectance and enhanced illuminance components are then combined to produce an improved image. After that, the enhanced image is mosaicked using multiband blending. Finally, the intensity differences between the enhanced images are further decreased using the block-based Wallis transform based on the mosaic. We assessed the proposed method on 402 Sentinel-1 images covering the majority of China's land area to verify its robustness. When compared to similar algorithms, our strategy reduces the color distance by about 36.72%, improves the average gradient by around 89.44%, and increases the patch-based contrast quality index by roughly 32.85%. The experimental outcomes reveal that our approach has considerable advantages in terms of color correction and image quality improvement, both visually and quantitatively.

Keywords

• Color correction
• naturalness restoration
• remote sensing images
• Retinex theory