Multi-Resolution Transform Based Image Fusion for Underwater Image Enhancement

Underwater images captured by imaging equipment are affected by factors such as absorption, scattering, and illumination, resulting in very poor image quality. In this paper, a method for enhancing underwater images based on dual-tree complex wavelet transform (DT-CWT) is proposed for improving the visual features of underwater images. Adaptive histogram equalization like CLAHE and multi-scale retinex are employed on the frequency components to assist in enhancing the brightness and contrast of the underwater image. The haze in the image is reduced by color correction provided by multiscale retinex algorithm. The enhanced image is reconstructed by combining the processed low-frequency and high-frequency components using the inverse transform. The proposed method is evaluated on two underwater image datasets UIEB and EUVP and the results are compared with existing methods. The experimental results demonstrate that the suggested method significantly enhances the quality and visibility of underwater images. The proposed approach surpasses state-of-the-art methods in subjective and objective aspects.