A novel deep neural network for noise removal from underwater image

Abstract

Underwater image processing technologies have always been challenging tasks due to the complex underwater environment. Images captured under water are not only affected by the water itself, but also by the diverse suspended particles that increase the effect of absorption and scattering. Moreover, these particles themselves are usually imaged on the picture, causing the spot noise signal to interfere with the target objects. To address this issue, we propose a novel deep neural network for removing the spot noise from underwater images. Its main idea is to train a generative adversarial network (GAN) to transform the noisy image to clean image. Based on the deep encoder and decoder framework, the skip connections are introduced to combine the features of low-level and high-level to help recover the original image. Meanwhile, the self-attention mechanism is employed to the generative network to capture global dependencies in the feature maps, which can generate the image with fine details at every location. Furthermore, we apply the spectral normalization to both the generative and discriminative networks to stabilize the training process. Experiments evaluated on synthetic and real-world images show that the proposed method outperforms many recent state-of-the-art methods in terms of quantitative and visual quality. Besides, the results also demonstrate that the proposed method has the good ability to remove the spot noise from underwater images while preserving sharp edge and fine details.