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.
翻译:我们推出一个基于波流的双流网络,处理水下图像中的彩色和模糊细节。我们通过使用离散的波子变换将输入图像分解成多频带,将输入图像分解成多频带,产生下游结构图像和详细图像。这些子波段图像被用作我们双流网络的输入,这个双流网络包含两个子网络:多色空间聚变网络和细节增强网络。多色空间聚变网络将分解的结构图像作为输入,并利用输入的不同颜色空间的特征显示来估计颜色校正输出。详细增强网络通过改进高频子带的图像细节,处理原始水下图像的模糊性。我们验证了真实世界和合成水下数据集的拟议方法,并以低计算复杂度的方式展示了我们彩色校正和模糊清除模型的有效性。