论文标题
基于小波的双流网络，用于水下图像增强
A Wavelet-based Dual-stream Network for Underwater Image Enhancement
论文作者
论文摘要
我们提出了一个基于小波的双流网络，该网络解决了水下图像中的颜色铸造和模糊细节。我们通过使用离散小波变换将输入图像分解为多个频段，从而分别处理这些伪像，从而生成下采样的结构图像和详细图像。这些子频段图像被用作我们的双流网络的输入，该网络包含两个子网络：多色空间融合网络和详细信息增强网络。多色空间融合网络将分解的结构图像作为输入，并通过采用来自输入的各种颜色空间的特征表示来估算颜色校正的输出。详细信息增强网络通过改善高频次波带的图像细节来解决原始水下图像的模糊性。我们验证了对现实世界和合成水下数据集的提议方法，并显示了模型在颜色校正和模糊中的有效性，并且计算复杂性低。
We present a wavelet-based dual-stream network that addresses color cast and blurry details in underwater images. We handle these artifacts separately by decomposing an input image into multiple frequency bands using discrete wavelet transform, which generates the downsampled structure image and detail images. These sub-band images are used as input to our dual-stream network that incorporates two sub-networks: the multi-color space fusion network and the detail enhancement network. The multi-color space fusion network takes the decomposed structure image as input and estimates the color corrected output by employing the feature representations from diverse color spaces of the input. The detail enhancement network addresses the blurriness of the original underwater image by improving the image details from high-frequency sub-bands. We validate the proposed method on both real-world and synthetic underwater datasets and show the effectiveness of our model in color correction and blur removal with low computational complexity.
