Digital image processing domain is growing day-by-day by introducing novel technologies to provide assistance for several applications such as robotic activities, underwater network formation, and so on. In particular, underwater image processing is considered as the crucial task in image processing industry due to the flow of light waves that are not in the specific and expected range under the water level. While image restoration technology can adequately consider removing this same haze from source images, they need to obtain several images from a certain place that prevent it from being used in a real-time system. To overcome this issue, a deep study approach is developed by providing excellent outcomes of deep learning approaches in several other image analysis concerns such as colorizing images or object identification. A convolution neural network (CNN) model is trained to de-haze the individual images with image restoration in order to perform further with an image improvement. The proposed approach can produce images with image restoration quality by including a standard image input and here, the neural network is evaluated by using images and features, which are obtained from separate areas to prove its capacity to generalize. The efficiency of the proposed approach is high when compared to other existing methods

An Effectual Underwater Image Enhancement using Deep Learning Algorithm

Underwater image dehazing using global color features

Feature extraction of underwater images using principle component analysis with image registration

Deep learning is a core technology for sonar image classification. However, owing to the cost of sampling, a lack of data for sonar image classification impedes the training and deployment of classifiers. Classic deep learning models such as AlexNet, VGG, GoogleNet, and ResNet suffer from low recognition rates and overfitting. This paper proposes a novel network (ResNet-ACW) based on a residual network and a combined few-shot strategy, which is derived from generative adversarial networks (GAN) and transfer learning (TL). We establish a sonar image dataset of six-category targets, which are formed by sidescan sonar, forward-looking sonar, and three-dimensional imaging sonar. The training process of ResNet-ACW on the sonar image dataset is more stable and the classification accuracy is also improved through an asymmetric convolution and a designed network structure. We design a novel GAN (LN-PGAN) that can generate images more efficiently to enhance our dataset and fine-tune ResNet-ACW pretrained on mini-ImageNet. Our method achieves 95.93% accuracy and a 14.19% increase in the six-category target sonar image classification tasks.

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Small-Sample Sonar Image Classification Based on Deep Learning

A novel underwater picture enhancement approach under non-uniform lighting is presented to solve the issues of underwater photographs with unevenness due to additional lighting in deep-sea and night-time environments. Water suspended particles can cause image noise, low contrast, and colour deviation. The heterogeneous feature fusion module aims to combine multiple levels and levels of features with improving the network's ability to perceive semantic and specific information. The capability of autonomous underwater and remotely driven cars to explore and comprehend their environments is contingent on improving underwater images, a crucial low-level computer vision challenge. Recent applications of deep learning models include enhancing aquatic image quality and resolving several computer vision problems. Although several deep learning-based techniques exist for enhancing underwater images, their implementation is challenging due to the high memory and model parameter requirements. We propose a solution based on radial basis function networks (RBFN) for lightweight multiscale data fusion (LMFS). The LMFS incorporates diverse branches with varying kernel sizes to generate multiscale feature maps. The proposed RBFN-LMFS The convolution layer with jump connection and the attention module produces the output from the feature extraction module, which aims to extract various features at the network's beginning. The outcomes of our experiments on diverse data sets demonstrate that our proposed RBFN-LMFS technique performs well in processing both synthetic and authentic underwater images and successfully recovers image colour and texture characteristics. The visual output is superior to existing underwater image enhancement algorithms and is consistent with aspects of human vision. 

Radial basis function networks with lightweight multiscale fusion strategy‐based underwater image enhancement

Underwater image enhancement is an active area of research due to its wide applications in areas like marine research, automated underwater vehicles, etc. In general, the underwater images have low contrast, blurriness, and color cast due to various effects like absorption, scattering, and refraction. Normally, the underwater images are less clear and not suitable for various applications. The underwater images should be enhanced to use for real-life applications and the natural image enhancement techniques may not work well for underwater images. In this paper, we introduce a scheme for enhancing the quality of the underwater images by using a residual neural network (ResNet). The synthetic underwater images for the experimental study are generated using the underwater generative adversarial network (UWGAN). The experimental results show that the new underwater image enhancement techniques perform well, and it can be used for real-life applications where we need good quality underwater images.

A Novel Underwater Image Enhancement Technique using ResNet

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A Novel Underwater Image Enhancement Technique using ResNet