Coastal and estuarine applications of multispectral photography

An evaluation of multispectral photographic techniques for optical penetration of water in the northeastern United States and the Gulf of Mexico coastal waters is presented. The spectral band (493 to 543 nanom), when exposed to place the water mass at about unit density on the photographic emulsion, was found to provide the best water penetration, independent of altitude or time of day, as long as solar glitter from the surface of the water is avoided. An isoluminous color technique was perfected, which eliminates the dimension of brightness from a multispectral color presentation

Improving Mix-CLAHE with ACO for Clearer Oceanic Images

Oceanic pictures have poor visibility attributable to various factors; weather disturbance, particles in water, lightweight frames and water movement which results in degraded and low contrast pictures of underwater. Visibility restoration refers to varied ways in which aim to decline and remove the degradation that have occurred whereas the digital image has been obtained. The probabilistic Ant Colony Optimization (ACO) approach is presented to solve the problem of designing an optimal route for hard combinatorial problems. It\u27s found that almost all of the prevailing researchers have neglected several problems i.e. no technique is correct for various reasonably circumstances. the prevailing strategies have neglected the utilization of hymenopter colony optimization to cut back the noise and uneven illuminate downside. The main objective of this paper is to judge the performance of ANT colony optimization primarily based haze removal over the obtainable MIX-CLAHE (Contrast Limited adaptive histogram Equalization) technique. The experiment has clearly showed the effectiveness of the projected technique over the obtainable strategies

Image enhancement for underwater mining applications

The exploration of water bodies from the sea to land filled water spaces has seen a continuous increase with new technologies such as robotics. Underwater images is one of the main sensor resources used but suffer from added problems due to the environment. Multiple methods and techniques have provided a way to correct the color, clear the poor quality and enhance the features. In this thesis work, we present the work of an Image Cleaning and Enhancement Technique which is based on performing color correction on images incorporated with Dark Channel Prior (DCP) and then taking the converted images and modifying them into the Long, Medium and Short (LMS) color space, as this space is the region in which the human eye perceives colour. This work is being developed at LSA (Laboratório de Sistema Autónomos) robotics and autonomous systems laboratory. Our objective is to improve the quality of images for and taken by robots with the particular emphasis on underwater flooded mines. This thesis work describes the architecture and the developed solution. A comparative analysis with state of the art methods and of our proposed solution is presented. Results from missions taken by the robot in operational mine scenarios are presented and discussed and allowing for the solution characterization and validation

A Non-Reference Evaluation of Underwater Image Enhancement Methods Using a New Underwater Image Dataset

The rise of vision-based environmental, marine, and oceanic exploration research highlights the need for supporting underwater image enhancement techniques to help mitigate water effects on images such as blurriness, low color contrast, and poor quality. This paper presents an evaluation of common underwater image enhancement techniques using a new underwater image dataset. The collected dataset is comprised of 100 images of aquatic plants taken at a shallow depth of up to three meters from three different locations in the Great Lake Superior, USA, via a Remotely Operated Vehicle (ROV) equipped with a high-definition RGB camera. In particular, we use our dataset to benchmark nine state-of-the-art image enhancement models at three different depths using a set of common non-reference image quality evaluation metrics. Then we provide a comparative analysis of the performance of the selected models at different depths and highlight the most prevalent ones. The obtained results show that the selected image enhancement models are capable of producing considerably better-quality images with some models performing better than others at certain depths

Use of ERTS-1 data in the educational and applied research programs of agricultural extension

There are no author-identified significant results in this report

Object Enhancement, Noise Reduction, Conversion and Collection of Spatiotemporal Image Data

In this report, a variety of cellular dynamics are enhanced and analyzed utilizing various algorithms and filter for contrast enhancement. This report will also illustrate the underlying complexities of processing compressed data received from certain type of sensors, their default applications, various methods in converting compressed data to compatible universal uncompressed formats allowed in scientific applications, various methods of image and video capture, guidelines in ethical image manipulation, various methods of frame extraction, and analyzing/processing video images. These methods and processes purposely utilize freeware and public domain software to lower the cost of reproducibility for all

Physics-Aware Semi-Supervised Underwater Image Enhancement

Underwater images normally suffer from degradation due to the transmission medium of water bodies. Both traditional prior-based approaches and deep learning-based methods have been used to address this problem. However, the inflexible assumption of the former often impairs their effectiveness in handling diverse underwater scenes, while the generalization of the latter to unseen images is usually weakened by insufficient data. In this study, we leverage both the physics-based underwater Image Formation Model (IFM) and deep learning techniques for Underwater Image Enhancement (UIE). To this end, we propose a novel Physics-Aware Dual-Stream Underwater Image Enhancement Network, i.e., PA-UIENet, which comprises a Transmission Estimation Steam (T-Stream) and an Ambient Light Estimation Stream (A-Stream). This network fulfills the UIE task by explicitly estimating the degradation parameters of the IFM. We also adopt an IFM-inspired semi-supervised learning framework, which exploits both the labeled and unlabeled images, to address the issue of insufficient data. Our method performs better than, or at least comparably to, eight baselines across five testing sets in the degradation estimation and UIE tasks. This should be due to the fact that it not only can model the degradation but also can learn the characteristics of diverse underwater scenes.Comment: 12 pages, 5 figure