Enhancement of Imagery in Poor Visibility Condition by Using GUI

Our focus in this work will be primarily in examples of enhancements in poor weather condition. GUI will be made in order for better user interference. These tools classify the overall brightness, contrast, and sharpness of an image based upon its regional statistics. Wavelet transform is the most exciting development in the last decade. The method focuses on wavelet-based image resolution enhancement and suitable for processing the image/video resolution enhancement. The Software tool used is MATLAB

UG^2: a Video Benchmark for Assessing the Impact of Image Restoration and Enhancement on Automatic Visual Recognition

Advances in image restoration and enhancement techniques have led to discussion about how such algorithmscan be applied as a pre-processing step to improve automatic visual recognition. In principle, techniques like deblurring and super-resolution should yield improvements by de-emphasizing noise and increasing signal in an input image. But the historically divergent goals of the computational photography and visual recognition communities have created a significant need for more work in this direction. To facilitate new research, we introduce a new benchmark dataset called UG^2, which contains three difficult real-world scenarios: uncontrolled videos taken by UAVs and manned gliders, as well as controlled videos taken on the ground. Over 160,000 annotated frames forhundreds of ImageNet classes are available, which are used for baseline experiments that assess the impact of known and unknown image artifacts and other conditions on common deep learning-based object classification approaches. Further, current image restoration and enhancement techniques are evaluated by determining whether or not theyimprove baseline classification performance. Results showthat there is plenty of room for algorithmic innovation, making this dataset a useful tool going forward.Comment: Supplemental material: https://goo.gl/vVM1xe, Dataset: https://goo.gl/AjA6En, CVPR 2018 Prize Challenge: ug2challenge.or

An Efficient Comparative Analysis of CNN-based Image Classification in the Jupyter Tool Using Multi-Stage Techniques

The main process of this image classification with a convolution neural network using deep learning model was performed in the programming language Python code in the Jupyter tool, mainly using the data set of IRS P-6 LISS IV from an Indian remote sensing satellite with a high resolution multi-spectral camera with around 5.8m from an 817 km altitude Delhi image. To classify the areas within the cropped image required to apply enhancement techniques, the image size was 1000 mb. To view this image file required high-end software for opening. For that, initially, ERDAS imaging software viewer was used for cropping into correct resolution pixels. based on that cropped image used for image classification with preprocessing for applying filters for enhancement. And with the convolution neural network model, required to train the sample images of the same pixels, was collected from the group of objects that were cropped. Then we needed to use image sample areas to train the model with learning rate and epoch rate to improve object detection accuracy using the Jupyter notebook tool with tensorflow and machine learning model produce the accuracy rate of 90.78%

Edge Enhancement Investigations by Means of Experiments and Simulations

Standard neutron imaging procedures are based on the “shadow” of the transmitted radiation, attenuated by the sample material. Under certain conditions significant deviations from pure transmission can be found in the form of enhancement or depression at the edges of the samples. These effects can limit the quantification process in the related regions. Otherwise, an enhancement and improvement of visibility can be achieved e.g. in defect analysis. In systematic studies we investigated the dependency of these effects on the specific material (mainly for common metals), such as the sample-to-detector distance, the beam collimation, the material thickness and the neutron energy. The beam lines ICON and BOA at PSI and ANTARES at TU München were used for these experiments due to their capability for neutron imaging with highest possible spatial resolution (6.5 to 13.5 micro-meter pixel size, respectively) and their cold beam spectrum. Next to the experimental data we used a McStas tool for the description of refraction and reflection features at edges for comparison. Even if minor contributions by coherent in-line propagation phase contrast are underlined, the major effect can be described by refraction of the neutrons at the sample-void interface. Ways to suppress and to magnify the edge effects can be derived from these findings.Fil: Lehmann, E.. Paul Scherrer Institut; SuizaFil: Schulz, M.. Technische Universitat Munchen; AlemaniaFil: Wang, Y.. China Insititute of Atomic Energy; ChinaFil: Tartaglione, Aureliano. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Neurofeedback-Based Moral Enhancement and the Notion of Morality

Some skeptics question the very possibility of moral bioenhancement by arguing that if we lack a widely acceptable notion of morality, we will not be able to accept the use of a biotechnological technique as a tool for moral bioenhancement. I will examine this skepticism and argue that the assessment of moral bioenhancement does not require such a notion of morality. In particular, I will demonstrate that this skepticism can be neutralized in the case of recent neurofeedback techniques. This goal will be accomplished in four steps. First, I will draw an outline of the skepticism against the possibility of moral bioenhancement and point out that a long-lasting dispute among moral philosophers nourishes this skepticism. Second, I will survey recent neurofeedback techniques and outline their three features: the variety of the target human faculties, such as emotion, cognition, and behavior; the flexibility or personalizability of the target brain state; and the nonclinical application of neurofeedback techniques. Third, I will argue that, by virtue of these three unique features, neurofeedback techniques can be a tool for moral bioenhancement without adopting any specific notion of morality. Fourth, I will examine the advantages and threats that neurofeedback-based moral enhancement may have. Finally, I will conclude that neurofeedback-based moral enhancement can become a new and promising tool for moral bioenhancement and requires further ethical investigations on its unique features

Reflectance Transformation Imaging (RTI) System for Ancient Documentary Artefacts

This tutorial summarises our uses of reflectance transformation imaging in archaeological contexts. It introduces the UK AHRC funded project reflectance Transformation Imaging for Anciant Documentary Artefacts and demonstrates imaging methodologies