Large Format, High Resolution Image Sensors

1987 ◽  
Vol 26 (9) ◽  
Author(s):  
Morley M. Blouke ◽  
B. Corrie ◽  
Denis L. Heidtmann ◽  
F. H. Yang ◽  
M. Winzenread ◽  
...  
Keyword(s):  
High Resolution ◽  
Image Sensors ◽  
Large Format ◽  
Resolution Image ◽  
High Resolution Image

scholarly journals Energy-Aware High Resolution Image Acquisition via Heterogeneous Image Sensors

2008 ◽  
Vol 2 (4) ◽  
pp. 526-537 ◽  
Author(s):  
Hyungjin Kim ◽  
M. Rahimi ◽  
Dong-U Lee ◽  
D. Estrin ◽  
J.D. Villasenor
Keyword(s):  
High Resolution ◽  
Image Acquisition ◽  
Image Sensors ◽  
Energy Aware ◽  
Resolution Image ◽  
High Resolution Image

PureView Technology (Optimisation & Evaluation Techniques in Image Processing)

2015 ◽  
Vol 3 (2) ◽  
Author(s):  
Jyoti Prakash Patra ◽  
Puru Agrawal
Keyword(s):  
Image Processing ◽  
High Resolution ◽  
High Performance ◽  
Image Sensor ◽  
Image Sensors ◽  
Resolution Image ◽  
High Resolution Image ◽  
Lighting Conditions ◽  
High Quality Image ◽  
Evaluation Techniques

PureView Technology is the combination of a super high-resolution image sensor and high-performance optics. It further applies advanced image processing algorithms and pixel oversampling to give the best quality outputs. It uses pixel oversampling method. Pixel oversampling combines many pixels to create a single (super) pixel. When this happens, we keep virtually all the details but filter away visual noise from the image. The speckled, grainy look we tend to get in low-lighting conditions is greatly reduced. One of the major benefits of this technology is loss-less zoom. The level of pixel oversampling is highest when we are not using the zoom. It gradually decreases until we hit maximum zoom, where there is no oversampling. This technique thus allows us to have loss-less zooms even when we are using the camera for taking zoomed in photos. The core of this technology lies somewhere in the satellite imagery system which uses a similar method of pixel oversampling and high-resolution image sensors. With PureView, uses a system called oversampling, which takes the original greater number of megapixels captured with the enormous sensor and reduces them to a high-quality image consisting of only a few megapixels. Pixels are pulled together into groups of seven and those seven pixels are then condensed into one, so that even though the resulting photograph is only a few megapixel images it is of a better quality than those captured with more traditional five megapixel cameras. For example, Nokia Lumia 1020 uses a 41-megapixel camera to take the original image, however, reduces this to only an output of 5 megapixels. This thus produces a

Radiation Damage, Contrast and Statistics in High Resolution Biological Electron Microscopy

1970 ◽  
Vol 28 ◽  
pp. 260-261
Author(s):  
Robert M. Glaeser
Keyword(s):  
High Resolution ◽  
Particle Flux ◽  
Unit Area ◽  
Signal To Noise ◽  
Resolution Image ◽  
High Resolution Image ◽  
Pass Through ◽  
Counting Statistics ◽  
The Relationship ◽  
Picture Element

It is well known that a large flux of electrons must pass through a specimen in order to obtain a high resolution image while a smaller particle flux is satisfactory for a low resolution image. The minimum particle flux that is required depends upon the contrast in the image and the signal-to-noise (S/N) ratio at which the data are considered acceptable. For a given S/N associated with statistical fluxtuations, the relationship between contrast and “counting statistics” is s131_eqn1, where C = contrast; r2 is the area of a picture element corresponding to the resolution, r; N is the number of electrons incident per unit area of the specimen; f is the fraction of electrons that contribute to formation of the image, relative to the total number of electrons incident upon the object.

New method for tree stands parameters determination using high resolution images done by unmanned aerial vehicle

2016 ◽  
Author(s):  
А.С. Алексеев ◽  
А.А. Никифоров ◽  
А.А. Михайлова ◽  
М.Р. Вагизов
Keyword(s):  
High Resolution ◽  
Unmanned Aerial Vehicle ◽  
New Method ◽  
Growth And Yield ◽  
Special Treatment ◽  
Resolution Image ◽  
Growing Stock ◽  
High Resolution Image ◽  
Aerial Vehicle ◽  
Area Unit

В связи со старением информационных материалов о состоянии лесов существует потребность в разработке новых методов таксации древостоев, основанных на применении последних научно-технических достижений в области теории структуры и продуктивности древостоев, дистанционных методов изучения лесов, информационных и ГИС технологий. В статье приведены результаты разработки и проверки нового метода определения таксационных характеристик сомкнутых насаждений на основе правила 3/2 и подобных ему правил Хильми и Рейнеке, с одной стороны, и определения числа деревьев на единице площади по снимку сверх высокого разрешения, полученного с помощью БПЛА, с другой. С теоретической точки зрения эта зависимости величин запаса, средней высоты и среднего диаметра от числа стволов на единице площади относятся к классу аллометрических связей, очень часто встречающихся при количественном описании соотношений частей биологических систем разных уровней иерархии, от организмов до экосистем. Параметры аллометрических зависимостей запаса, средних высоты и диаметра от числа стволов на единице площади были определены для основных лесообразующих пород по данным таблиц хода роста нормальных (полных) древостоев с теоретическим показателем степени и затем использованы для расчетов. Число деревьев на единице площади определялось по снимку с разрешением 7,13 см/пиксель, полученному с помощью 4-роторной платформы. Обработка материалов аэрофотосъемки была выполнена в специализированной фотограмметрической системе Agisoft Photoscan. В результате были получены ортофотоплан и цифровая модель поверхности крон деревьев на изучаемую территорию с определением их высот. Для автоматизированной обработки полученных изображений с целью получения значений числа деревьев на единицу площади был создан специализированный скрипт на языке Java. Погрешности определения таксационных характеристик древостоев предлагаемым методом не выше установленных действующими нормативными материалами. Every time there is a demand for new innovative methods of forest resources estimation based on last achievements in theoretical science, remote sensing methods, information and GIS-technologies. In the paper are presented a new method and the results of its application to forest stands growing stock, mean height and diameter determination. The method is based on rule 3/2 and similar Reineke and Hilmy rules, on one hand and high resolution image made by unmanned aerial vehicle, which used for determination of number of trees per area unit, on other. The above rules are well known in quantitative biology as an allometric and widely used for description of different kind of relations in biological systems of various scale: from organisms to ecosystems. Parameters of above allometric relationships between growing stock, mean height and diameter and stems density per area unit was determine on the base of full stock growth and yield tables for main tree species and after used for experimental calculations. The number of trees per area unit was determined after special treatment of high resolution image made by unmanned flying machine. The growing stock, mean height and diameter determined by suggested method was compared with the data of regular forest inventory. Comparison gives positive result and method may be recommended for further development.

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