DBMKA-Net:Dual branch multi-perception kernel adaptation for underwater image enhancement

Abstract

In recent years, due to the dependence on wavelength-based light absorption and scattering, underwater photographs captured by devices often exhibit characteristics such as blurriness, faded color tones, and low contrast. To address these challenges, convolutional neural networks (CNNs) with their robust feature-capturing capabilities and adaptable structures have been employed for underwater image enhancement. However, most CNN-based studies on underwater image enhancement have not taken into account color space kernel convolution adaptability, which can significantly enhance the model’s expressive capacity. Building upon current academic research on adjusting the color space size for each perceptual field, this paper introduces a Double-Branch Multi-Perception Kernel Adaptive (DBMKA) model. A DBMKA module is constructed through two perceptual branches that adapt the kernels: channel features and local image entropy. Additionally, considering the pronounced attenuation of the red channel in underwater images, a Dependency-Capturing Feature Jump Connection module (DCFJC) has been designed to capture the red channel’s dependence on the blue and green channels for compensation. Its skip mechanism effectively preserves color contextual information. To better utilize the extracted features for enhancing underwater images, a Cross-Level Attention Feature Fusion (CLAFF) module has been designed. With the Double-Branch Multi-Perception Kernel Adaptive model, Dependency-Capturing Skip Connection module, and Cross-Level Adaptive Feature Fusion module, this network can effectively enhance various types of underwater images. Qualitative and quantitative evaluations were conducted on the UIEB and EUVP datasets. In the color correction comparison experiments, our method demonstrated a more uniform red channel distribution across all gray levels, maintaining color consistency and naturalness. Regarding image information entropy (IIE) and average gradient (AG), the data confirmed our method’s superiority in preserving image details. Furthermore, our proposed method showed performance improvements exceeding 10% on other metrics like MSE and UCIQE, further validating its effectiveness and accuracy.