Underwater Aerial Vehicle Networks Based Image Analysis By Deep Learning Architecture Integrated With 5G System

With its astonishing ability to learn representation from data, deep neural networks (DNNs) have made efficient advances in the processing of pictures, time series, spoken language, audio, video, and many other types of data.In an effort to compile the volume of information generated in remote sensing field's subfields, surveys and literature revisions explicitly concerning DNNs methods applications are carried out Aerial sensing research has recently been dominated by applications based on Unmanned Aerial Vehicles (UAVs).There hasn't yet been a literature review that integrates the "deep learning" and "UAV remote sensing" thematics.This research propose novel technique in underwater aerial vehicle networks based image analysis by feature extraction and classification utilizing DL methods. here UAV based images through on 5G module is collected and this image has been processed for noise removal, smoothening and normalization. The processed image features has been extracted using multilayer extreme learning based convolutional neural networks. Then extracted deep features has been classified utilizingrecursive elimination based radial basis function networks. The experimental analysis is carried out based on numerous UAV image dataset in terms of accuracy, precision, recall, F-measure, RMSE and MAP.Proposed method attained accuracy of 96%, precision of 94%, recall of 85%, F- measure of 72%, RMSE of 48%, MAP of 41%

PRIDNet based Image Denoising for Underwater Images

Underwater image enhancement has become a popular research topic due to its importance in aquatic robotics and marine engineering. However, the underwater images frequently experience signal-dependent speckle noise when transmitting and acquiring data, which can limit certain applications such as detection, object tracking. In the recent years, the existing underwater image enhancement algorithms efficiency has been analysed and evaluated on a small number of carefully chosen real-world images or synthetic datasets. As such, it is challenging to predict how these algorithms might function with images acquired in the wild under various circumstances. This paper introduces a new solution for noise removal from underwater images called Pyramid Real Image Noise Removal Network (PRIDNet) with patches.PRIDNet is a three-level network design using image patches. The tests were carried out on a dataset of actual noisy images demonstrate that, in terms of quantitative metrics, our proposed denoising model reduction performs better with the exixting denoisers. We determine the effectiveness and constraints of existing algorithms using benchmark assessments and the suggested model, offering valuable information for further studies on underwater image enhancement

Learning to Interpret Fluid Type Phenomena via Images

Learning to interpret fluid-type phenomena via images is a long-standing challenging problem in computer vision. The problem becomes even more challenging when the fluid medium is highly dynamic and refractive due to its transparent nature. Here, we consider imaging through such refractive fluid media like water and air. For water, we design novel supervised learning-based algorithms to recover its 3D surface as well as the highly distorted underground patterns. For air, we design a state-of-the-art unsupervised learning algorithm to predict the distortion-free image given a short sequence of turbulent images. Specifically, we design a deep neural network that estimates the depth and normal maps of a fluid surface by analyzing the refractive distortion of a reference background pattern. Regarding the recovery of severely downgraded underwater images due to the refractive distortions caused by water surface fluctuations, we present the distortion-guided network (DG-Net) for restoring distortion-free underwater images. The key idea is to use a distortion map to guide network training. The distortion map models the pixel displacement caused by water refraction. Furthermore, we present a novel unsupervised network to recover the latent distortion-free image. The key idea is to model non-rigid distortions as deformable grids. Our network consists of a grid deformer that estimates the distortion field and an image generator that outputs the distortion-free image. By leveraging the positional encoding operator, we can simplify the network structure while maintaining fine spatial details in the recovered images. We also develop a combinational deep neural network that can simultaneously perform recovery of the latent distortion-free image as well as 3D reconstruction of the transparent and dynamic fluid surface. Through extensive experiments on simulated and real captured fluid images, we demonstrate that our proposed deep neural networks outperform the current state-of-the-art on solving specific tasks

Fully Point-wise Convolutional Neural Network for Modeling Statistical Regularities in Natural Images

Modeling statistical regularity plays an essential role in ill-posed image processing problems. Recently, deep learning based methods have been presented to implicitly learn statistical representation of pixel distributions in natural images and leverage it as a constraint to facilitate subsequent tasks, such as color constancy and image dehazing. However, the existing CNN architecture is prone to variability and diversity of pixel intensity within and between local regions, which may result in inaccurate statistical representation. To address this problem, this paper presents a novel fully point-wise CNN architecture for modeling statistical regularities in natural images. Specifically, we propose to randomly shuffle the pixels in the origin images and leverage the shuffled image as input to make CNN more concerned with the statistical properties. Moreover, since the pixels in the shuffled image are independent identically distributed, we can replace all the large convolution kernels in CNN with point-wise ($1*1$) convolution kernels while maintaining the representation ability. Experimental results on two applications: color constancy and image dehazing, demonstrate the superiority of our proposed network over the existing architectures, i.e., using 1/10$\sim$1/100 network parameters and computational cost while achieving comparable performance.Comment: 9 pages, 7 figures. To appear in ACM MM 201