DAPNet: Dual Attention Probabilistic Network for Underwater Image Enhancement

Abstract

Underwater images frequently experience issues, such as color casts, loss of contrast, and overall blurring due to the impact of light attenuation and scattering. To tackle these degradation issues, we present a highly efficient and robust method for enhancing underwater images, called DAPNet. Specifically, we integrate the extended information block into the encoder to minimize information loss during the downsampling stage. Afterward, we incorporate the dual attention module to enhance the network's sensitivity to critical location information and essential channels while utilizing codecs for feature reconstruction. Simultaneously, we employ adaptive instance normalization to transform the output features and generate multiple samples. Lastly, we utilize Monte Carlo likelihood estimation to obtain stable enhancement results from this sample space, ensuring the consistency and reliability of the final enhanced image. Experiments are conducted on three underwater image data sets to validate our method's effectiveness. Moreover, our method demonstrates strong performance in underwater image enhancement and exhibits excellent generalization and effectiveness in tasks, such as low-light image enhancement and image dehazing.