Digital differential hysteresis iimage processing displays what the microscope acquires but the eye can't see

1995 ◽  
Vol 53 ◽  
pp. 642-643
Author(s):  
Klaus-Ruediger Peters
Keyword(s):  
Image Processing ◽  
Visual System ◽  
High Precision ◽  
Image Data ◽  
Processing Technology ◽  
Output Data ◽  
Contrast Resolution ◽  
Pixel Intensity ◽  
Data Reading ◽  
Hysteresis Properties

Differential hysteresis processing is a new image processing technology that provides a tool for the display of image data information at any level of differential contrast resolution. This includes the maximum contrast resolution of the acquisition system which may be 1,000-times higher than that of the visual system (16 bit versus 6 bit). All microscopes acquire high precision contrasts at a level of <0.01-25% of the acquisition range in 16-bit - 8-bit data, but these contrasts are mostly invisible or only partially visible even in conventionally enhanced images. The processing principle of the differential hysteresis tool is based on hysteresis properties of intensity variations within an image.Differential hysteresis image processing moves a cursor of selected intensity range (hysteresis range) along lines through the image data reading each successive pixel intensity. The midpoint of the cursor provides the output data. If the intensity value of the following pixel falls outside of the actual cursor endpoint values, then the cursor follows the data either with its top or with its bottom, but if the pixels' intensity value falls within the cursor range, then the cursor maintains its intensity value.

The Research of Precision Detection System on Grade Rode Based on Image Processing Technology

2014 ◽  
Vol 556-562 ◽  
pp. 2591-2594
Author(s):  
Hui Feng Kang ◽  
Qian Qian Hou
Keyword(s):  
Image Processing ◽  
High Precision ◽  
Image Acquisition ◽  
Processing Technology ◽  
System Stability ◽  
Sensor System ◽  
Calibration System ◽  
Verification System ◽  
Image Processing Technology ◽  
Test Requirements

In this article, a level ruler Precision Verification System program of the mobile-camera image processing technology that is based on the camera mobile image processing technology is proposed, as a result of the research for the existing barcode type level ruler verification system and a high-precision grating sensor system hardware structure is also built. Secondly, we calculate the uncertainty of the system, and at last do the system stability test that the results show that the system achieves the test requirements. On the barcode type level existing ruler verification system research, proposed a camera mobile image processing technology based on the level ruler of precision calibration system, builds a detection sensor system with high precision grating, realizes automatic acquisition level ruler barcode image, method of application without barcode image with barcode image subtraction the preliminary processing of image acquisition, eliminate the influence of background light on the image acquisition, the uncertainty of the calibration system of the theoretical calculation and experimental verification, the results show that the system achieves the test requirements.

Information and estimation in image processing

1987 ◽  
Vol 45 ◽  
pp. 14-17
Author(s):  
B. Roy Frieden
Keyword(s):  
Image Processing ◽  
Optical System ◽  
Large Amplitude ◽  
Communication Theory ◽  
Image Data ◽  
Direct Approach ◽  
Ill Posed

Despite the skill and determination of electro-optical system designers, the images acquired using their best designs often suffer from blur and noise. The aim of an “image enhancer” such as myself is to improve these poor images, usually by digital means, such that they better resemble the true, “optical object,” input to the system. This problem is notoriously “ill-posed,” i.e. any direct approach at inversion of the image data suffers strongly from the presence of even a small amount of noise in the data. In fact, the fluctuations engendered in neighboring output values tend to be strongly negative-correlated, so that the output spatially oscillates up and down, with large amplitude, about the true object. What can be done about this situation? As we shall see, various concepts taken from statistical communication theory have proven to be of real use in attacking this problem. We offer below a brief summary of these concepts.

High-precision Classification of Small Insects with Similar Color and Shape Using Image Processing

2016 ◽  
Vol 136 (8) ◽  
pp. 1120-1127 ◽  
Author(s):  
Naoya Ikemoto ◽  
Kenji Terada ◽  
Yuta Takashina ◽  
Akio Nakano
Keyword(s):  
Image Processing ◽  
High Precision

MEASUREMENT METHOD FOR WAVE RUNUP HEIGHT USING IMAGE PROCESSING TECHNOLOGY

2019 ◽  
Vol 75 (2) ◽  
pp. I_1267-I_1272
Author(s):  
Naoki FUKUHARA ◽  
Tetsuya TAKESHITA ◽  
Fuminori KATO ◽  
Takuya TERANISHI ◽  
Takahiro AKITA ◽  
...  
Keyword(s):  
Image Processing ◽  
Measurement Method ◽  
Processing Technology ◽  
Wave Runup ◽  
Image Processing Technology ◽  
Runup Height

scholarly journals Diabetic Retinal Grading Using Attention-Based Bilinear Convolutional Neural Network and Complement Cross Entropy

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 816
Author(s):  
Pingping Liu ◽  
Xiaokang Yang ◽  
Baixin Jin ◽  
Qiuzhan Zhou
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Network Model ◽  
Rapid Development ◽  
Image Data ◽  
Lesion Detection ◽  
Great Success

Diabetic retinopathy (DR) is a common complication of diabetes mellitus (DM), and it is necessary to diagnose DR in the early stages of treatment. With the rapid development of convolutional neural networks in the field of image processing, deep learning methods have achieved great success in the field of medical image processing. Various medical lesion detection systems have been proposed to detect fundus lesions. At present, in the image classification process of diabetic retinopathy, the fine-grained properties of the diseased image are ignored and most of the retinopathy image data sets have serious uneven distribution problems, which limits the ability of the network to predict the classification of lesions to a large extent. We propose a new non-homologous bilinear pooling convolutional neural network model and combine it with the attention mechanism to further improve the network’s ability to extract specific features of the image. The experimental results show that, compared with the most popular fundus image classification models, the network model we proposed can greatly improve the prediction accuracy of the network while maintaining computational efficiency.

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