Underwater Image Enhancement via Adaptive Color Correction and Stationary Wavelet Detail Enhancement

High-quality images are of great significance for vision tasks in underwater environments. However, as light propagates through water, it is scattered and absorbed, which commonly causes issues like color distortion and loss of detail, making the capture of high-quality images challenging. To improve the quality of underwater images, we propose an underwater image enhancement method that is based on channel similarity to adaptive color correction and stationary wavelet detail enhancement. Specifically, We first innovatively introduce channel similarity values to avoid red artifacts during color correction, and finely adjust the compensation amount at the pixel level based on the intensity difference between the red and green channels. By designing a new dynamic normalization range based on channel similarity, our color correction method adaptively adjusts the dynamic range of each RGB channel’s pixel value. This accommodation for color deviations in various underwater scenes enhances the color saturation of images. Subsequently, using the stationary wavelet transform, we accurately decompose the image into low-frequency and high-frequency components. Through fine processing of the low-frequency components, we optimize detail performance and enhance the visual clarity of the underwater scene. Extensive experiments on four benchmark datasets validate that our method is state-of-the-art in underwater image enhancement, excelling in both qualitative and quantitative evaluations. Additionally, our method bolsters the precision of tasks such as keypoint matching and edge detection within the realm of image processing. The code is available at https://github.com/Zhenbo-Wang/Adaptive-Color-Correction-and-Stationary-Wavelet-Detail-Enhancement.