Deep Learning Methods for Synthetic Aperture Radar Image Despeckling: An Overview of Trends and Perspectives

Synthetic aperture radar (SAR) images are affected by a spatially correlated and signal-dependent noise called speckle, which is very severe and may hinder image exploitation. Despeckling is an important task that aims to remove such noise so as to improve the accuracy of all downstream image processing tasks. The first despeckling methods date back to the 1970s, and several model-based algorithms have been developed in the years since. The field has received growing attention, sparked by the availability of powerful deep learning models that have yielded excellent performance for inverse problems in image processing. This article surveys the literature on deep learning methods applied to SAR despeckling, covering both supervised and the more recent self-supervised approaches. We provide a critical analysis of existing methods, with the objective of recognizing the most promising research lines; identify the factors that have limited the success of deep models; and propose ways forward in an attempt to fully exploit the potential of deep learning for SAR despeckling

Plasma-arc-flow technology for sustainable treatment of high-impact fluid waste. A graphene-based material for industrial-wastewater purification

The research presented aimed to address the treatment of fluid waste with significant environmental impact by utilizing plasma technology, specifically plasma arc flow (PAF). The goal was to develop a novel purification material based on graphene for industrial applications and to optimize the treatment process. Analysis and monitoring of a submerged arc plasma reactor were the main goals of this research. This entailed a careful examination of the incoming wastewater that needed to be treated with the goal of identifying its precise composition characteristics with the relative tolerances needed for the reactions that were to follow in the reactor. The focus of the analysis was on input-parameter optimization, production of characteristic curves, and analysis of the factors affecting hydrogen evolution in syngas. Additionally, the study investigated how to determine the best viscosity for a particular input matrix by carrying out an evaluation study. The effects of this parameter were thought to be reduced by preheating the incoming wastewater through heat recovery. The long-term objective of the research is to create filters that can purify the water used and produced in gasification processes as well as to characterize the fixed reside from the gasifier for potential conversion into graphene-based material. In addition, this work acknowledges that additional experiments are required to validate its purifying capacity on wastewater produced by various industrial processes. Moreover, the inclusion of plans to model the evolution of hydrogen in PAF using the CHEMCAD software® and defining guidelines for optimizing parameters for enhanced energy efficiency showcased the research’s ambition to expand and refine its scope. Finding the best plant solutions that can significantly reduce electricity consumption is the ultimate goal. In summary, the study demonstrated significant advancement in the analysis and optimization of fluid-waste treatment with high environmental impact through the use of plasma technology, specifically PAF. A thorough and forward-looking approach was demonstrated by the use of modeling software, experimental studies, and plans for future research. The potential creation of graphene-based filters and the use of the fixed residue as a useful material further highlight the innovativeness of this research

Effect of Thiamine Status on Probability of Lake Ontario Chinook Salmon Spawning in the Upper or Lower Sections of Salmon River, New York

Consumption of thiaminase‐containing forage fishes reduces egg and muscle thiamine content and impairs the spawning migration of Cayuga Lake (New York) rainbow trout Oncorhynchus mykiss. Because some Chinook salmon O. tshawytscha from Lake Ontario have been shown to produce eggs low in thiamine, we examined the relationship between the migration of Chinook salmon and the thiamine content of their eggs spawned in the lower and upper sections of the Salmon River, a major tributary to Lake Ontario, in 2003–2006. Eggs from the upper section of the river were collected from 79 salmon returning to the state hatchery 25 river kilometers from the mouth. Eggs from 25 salmon in the lower section were collected from redds or females angled on redds approximately 1–3 km from the mouth. For all years combined, we found the mean thiamine concentration in eggs spawned in the lower section to be significantly lower than that for eggs spawned in the upper section; however, the annual differences in thiamine content of eggs between the upper and lower sections were significant only in 2003 and 2006. Binary logistic regression showed that the odds of spawning in the upper section was increased by 96% (95% confidence interval, 21–217%) for every nanomole of increase in the thiamine content of eggs. Therefore, the migratory achievement of Chinook salmon was significantly dependent on their thiamine status.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142217/1/nafm0895.pd

Chemical Linkage to Injected Tissues Is a Distinctive Property of Oxidized Avidin

We recently reported that the oxidized avidin, named AvidinOX®, resides for weeks within injected tissues as a consequence of the formation of Schiff's bases between its aldehyde groups and tissue protein amino groups. We also showed, in a mouse pre-clinical model, the usefulness of AvidinOX for the delivery of radiolabeled biotin to inoperable tumors. Taking into account that AvidinOX is the first oxidized glycoprotein known to chemically link to injected tissues, we tested in the mouse a panel of additional oxidized glycoproteins, with the aim of investigating the phenomenon. We produced oxidized ovalbumin and mannosylated streptavidin which share with avidin glycosylation pattern and tetrameric structure, respectively and found that neither of them linked significantly to cells in vitro nor to injected tissues in vivo, despite the presence of functional aldehyde groups. The study, extended to additional oxidized glycoproteins, showed that the in vivo chemical conjugation is a distinctive property of the oxidized avidin. Relevance of the high cationic charge of avidin into the stable linkage of AvidinOX to tissues is demonstrated as the oxidized acetylated avidin lost the property. Plasmon resonance on matrix proteins and cellular impedance analyses showed in vitro that avidin exhibits a peculiar interaction with proteins and cells that allows the formation of highly stable Schiff's bases, after oxidation

Noiseprint: A CNN-Based Camera Model Fingerprint

Forensic analyses of digital images rely heavily on the traces of in-camera and out-camera processes left on the acquired images. Such traces represent a sort of camera fingerprint. If one is able to recover them, by suppressing the high-level scene content and other disturbances, a number of forensic tasks can be easily accomplished. A notable example is the PRNU pattern, which can be regarded as a device fingerprint, and has received great attention in multimedia forensics. In this paper, we propose a method to extract a camera model fingerprint, called noiseprint, where the scene content is largely suppressed and model-related artifacts are enhanced. This is obtained by means of a Siamese network, which is trained with pairs of image patches coming from the same (label +1) or different (label -1) cameras. Although the noiseprints can be used for a large variety of forensic tasks, in this paper we focus on image forgery localization. Experiments on several datasets widespread in the forensic community show noiseprint-based methods to provide state-of-the-art performance

I difficili percorsi giudiziali del mobbing negli enti locali: notizie dalla Toscana

Il saggio esamina la giurisprudenza delle corti toscane in tema di mobbing negli enti locali

CNN-Based Fast Source Device Identification

Source identification is an important topic in image forensics, since it allows to trace back the origin of an image. This represents a precious information to claim intellectual property but also to reveal the authors of illicit materials. In this letter we address the problem of device identification based on sensor noise and propose a fast and accurate solution using convolutional neural networks (CNNs). Specifically, we propose a 2-channel-based CNN that learns a way of comparing camera fingerprint and image noise at patch level. The proposed solution turns out to be much faster than the conventional approach and to ensure an increased accuracy. This makes the approach particularly suitable in scenarios where large databases of images are analyzed, like over social networks. In this vein, since images uploaded on social media usually undergo at least two compression stages, we include investigations on double JPEG compressed images, always reporting higher accuracy than standard approaches

A comparison of flat and object-based transform coding techniques for the compression of multispectral images

In this work we implement and compare several state-of-the-art transform coding schemes for the compression of multispectral images, in order to better understand which elements have a deeper impact on the overall performance, and which tools guarantee the best results. All schemes are based on Karhunen-Löeve transform and/or Wavelet Transform, in various combinations, and use SPIHT as the coding engine. Moreover, besides the ordinary techniques, their object-based counterparts are also examined, so as to study the viability of such approach [1] for these images. Whenever possible, an optimal rate allocation strategy is applied. The experiments, performed on images acquired by two different sensors, highlight the superiority of KLT as spectral transform; the rough equivalence between object-based and ordinary techniques in terms of rate-distortion performance; and the importance of the optimal allocation. © 2005 IEEE