scholarly journals Window Memoization In Software As Applied To Image Processing Algorithms

2021 ◽  
Author(s):  
Tahir Jaffer
Keyword(s):  
Image Processing ◽  
Lower Bound ◽  
Detection Algorithm ◽  
Corner Detection ◽  
Image Processing Algorithm ◽  
Pass Filter ◽  
Pixel Intensity ◽  
Low Pass Filter ◽  
Low Pass ◽  
Speed Up

A new local image processing algorithm, the Tahir algorithm, is an adaptation to the standard low-pass filter. Its design is for images that have the spectrum of pixel intensity concentrated at the lower end of the intensity spectrum. Window memoization is a specialization of memoization. Memoization is a technique to reduce computational redundancy by skipping redundant calculations and storing results in memory. An adaptation for window memozation is developed based on improved symbol generation and a new eviction policy. On implementation, the mean lower-bound speed-up achieved was between 0.32 (slowdown of approximately 3) and 3.70 with a peak of 4.86. Lower-bound speed-up is established by accounting for the time to create and delete the cache. Window memoization was applied to: the convolution technique, Trajkovic corner detection algorithm and the Tahir algorithm. Window memoization can be evaluated by calculating both the speed-up achieved and the error introduced to the output image.

scholarly journals Window Memoization In Software As Applied To Image Processing Algorithms

2021 ◽  
Author(s):  
Tahir Jaffer
Keyword(s):  
Image Processing ◽  
Lower Bound ◽  
Detection Algorithm ◽  
Corner Detection ◽  
Image Processing Algorithm ◽  
Pass Filter ◽  
Pixel Intensity ◽  
Low Pass Filter ◽  
Low Pass ◽  
Speed Up

A new local image processing algorithm, the Tahir algorithm, is an adaptation to the standard low-pass filter. Its design is for images that have the spectrum of pixel intensity concentrated at the lower end of the intensity spectrum. Window memoization is a specialization of memoization. Memoization is a technique to reduce computational redundancy by skipping redundant calculations and storing results in memory. An adaptation for window memozation is developed based on improved symbol generation and a new eviction policy. On implementation, the mean lower-bound speed-up achieved was between 0.32 (slowdown of approximately 3) and 3.70 with a peak of 4.86. Lower-bound speed-up is established by accounting for the time to create and delete the cache. Window memoization was applied to: the convolution technique, Trajkovic corner detection algorithm and the Tahir algorithm. Window memoization can be evaluated by calculating both the speed-up achieved and the error introduced to the output image.

Based on the CO2 Gas Shielded Welding Molten Pool Image Edge Detection Algorithm

2013 ◽  
Vol 437 ◽  
pp. 840-844 ◽  
Author(s):  
Xiao Gang Liu ◽  
Bing Zhao
Keyword(s):  
Edge Detection ◽  
Molten Pool ◽  
High Speed ◽  
Vision System ◽  
Detection Algorithm ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass ◽  
Defect Recognition ◽  
Pool Image

This paper use the passive vision system through high-speed camera collects molten pool images; and then according to the frequency domain characteristics of the weld pool image Butterworth low-pass filter; gradient method for image enhancement obtained after pretreatment. Research Roberts, Sobel, Prewitt, Log, Zerocross, and Canny 6 both traditional differential operator edge detection processing results. Through comparison and analysis of choosing threshold for [0.1, 0. Canny operator can get the ideal molten pool edge character, for subsequent welding molten pool defect recognition provides favorable conditions.

scholarly journals Memristor bridge-based low pass filter for image processing

2019 ◽  
Vol 30 (03) ◽  
pp. 448-455 ◽  
Author(s):  
Yongbin Yu ◽  
◽  
Nijing Yang ◽  
Chenyu Yang ◽  
Tashi Nyima ◽  
...  
Keyword(s):  
Image Processing ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass

scholarly journals A New Simple Procedure for Extracting Coastline from SAR Image Based on Low Pass Filter and Edge Detection Algorithm

2021 ◽  
Vol 12 (3) ◽  
pp. 175
Author(s):  
Ni Nyoman Pujianiki ◽  
I Nyoman Sudi Parwata ◽  
Takahiro Osawa
Keyword(s):  
Simple Procedure ◽  
Detection Algorithm ◽  
Sar Image ◽  
Dual Polarization ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Edge Detection Algorithm ◽  
Low Pass ◽  
Pixel Value ◽  
Single Polarization

This study proposes a new simple procedure for extracting coastline from Synthetic Aperture Radar (SAR) images by utilizing a low-pass filter and edge detection algorithm. The low-pass filter is used to improve the histogram of the pixel value of the SAR image. It provides better distribution of pixel value and makes it easy to separate between sea and land surfaces. This study provides the processing steps using open-source software, i.e., SNAP SAR processor and QGIS application. This procedure has been tested using dual polarization Sentinel-1 (10x10 meters resolution) and single polarization ALOS-2 (3x3 meters resolution) dataset. The results show that using Sentinel-1 with dual polarization (VH) provides a better result than single polarization (VV). In the ALOS-2 case, only single polarization (HH) is available. However, even using only HH polarization, ALOS-2 provides a good result. In terms of resolution, ALOS-2 provides a better coastline than Sentinel-1 data due to ALOS-2 has better resolution. This procedure is expected to be helpful to detect coastline changes and for coastal area management.

scholarly journals Enhancing SNR and generating contrast for cryo-EM images with convolutional neural networks

2020 ◽  
Author(s):  
Eugene Palovcak ◽  
Daniel Asarnow ◽  
Melody G. Campbell ◽  
Zanlin Yu ◽  
Yifan Cheng
Keyword(s):  
Image Processing ◽  
High Frequency ◽  
Signal To Noise Ratio ◽  
Three Dimensional ◽  
Low Frequency ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Processing Pipeline ◽  
Visual Contrast ◽  
Low Pass

AbstractIn cryogenic electron microscopy (cryo-EM) of radiation-sensitive biological samples, both the signal-to-noise ratio (SNR) and the contrast of images are critically important in the image processing pipeline. Classic methods improve low-frequency image contrast experimentally, by imaging with high defocus, or computationally, by applying various types of low-pass filter. These contrast improvements typically come at the expense of high-frequency SNR, which is suppressed by high-defocus imaging and removed by low pass filtration. Here, we demonstrate that a convolutional neural network (CNN) denoising algorithm can be used to significantly enhance SNR and generate contrast in cryo-EM images. We provide a quantitative evaluation of bias introduced by the denoising procedure and its influences on image processing and three-dimensional reconstructions. Our study suggests that besides enhancing the visual contrast of cryo-EM images, the enhanced SNR of denoised images may facilitate better outcomes in the other parts of the image processing pipeline, such as classification and 3D alignment. Overall, our results provide a ground of using denoising CNNs in the cryo-EM image processing pipeline.

Dynamic smoothing in crosswell traveltime tomography

Geophysics ◽  
1997 ◽  
Vol 62 (1) ◽  
pp. 168-176 ◽  
Author(s):  
Tamas Nemeth ◽  
Egon Normark ◽  
Fuhao Qin
Keyword(s):  
Prediction Error ◽  
Error Function ◽  
Adaptive Regularization ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Smoothing Operator ◽  
Traveltime Tomography ◽  
Regularization Term ◽  
Low Pass ◽  
Speed Up

Variable‐size (dynamic) smoothing operator constraints are applied in crosswell traveltime tomography to reconstruct both the smooth‐ and fine‐scale details of the tomogram. In mixed and underdetermined problems a large number of iterations may be necessary to introduce the slowly varying slowness features into the tomogram. To speed up convergence, the dynamic smoothing operator applies adaptive regularization to the traveltime prediction error function with the help of the model covariance matrix. By so doing, the regularization term has a larger weight at initial iterations and the prediction error term dominates the final iterations with a small regularization term weight. In addition, it is shown that adaptive regularization acts by reweighting the adjoint modeling operator (preconditioning) and by providing additional damping. Comparisons of two dynamic smoothing operators, the low‐pass filter smoothing and the multigrid technique, with the fixed‐size (static) smoothing operators show that the dynamic smoothing operator yields more accurate velocity distributions with greater stability for larger velocity contrasts. Consequently, it is a preferred choice for regularization.

A quantum edge detection algorithm for quantum multi-wavelength images

2021 ◽  
pp. 2150017
Author(s):  
Engin Şahin ◽  
İhsan Yilmaz
Keyword(s):  
Edge Detection ◽  
Noise Reduction ◽  
Circuit Complexity ◽  
Detection Algorithm ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Edge Detection Algorithm ◽  
Detection Algorithms ◽  
Low Pass ◽  
Multi Wavelength

Quantum edge detection is one of the important part of quantum image processing. In this paper, a quantum edge detection algorithm is designed for the quantum representation of multi-wavelength image (QRMW) model. The algorithm includes all stages of filtering, enhancement and detection. The proposed algorithm is also designed to apply any filtering operation to QRMW images, not only for a particular filtering operation. The proposed algorithm aims to solve the problems that quantum edge detection algorithms in the literature have processing only for a particular operator and noise reduction. Moreover, the algorithm aims to perform operations more efficiently by using less resources. Low-pass filter (LPF) smoothing operators are applied in the filtering stage for the noise reduction problem. In order to apply all filtering operations to the image, arithmetic operators that can operate with all signed integers are used in the algorithm. The operators Sobel, Prewitt and Scharr in the enhancement stage and the gradient method in the detection stage are used for both verification of the proposed algorithm and comparisons with the existing algorithms. A method with quantitative outcomes is shown to evaluate the performance of the edge detection algorithms. Analysis of the simulations performed on sample images with different operators. The circuit complexity of the algorithm is presented and the comparisons are made with the existing studies. The superiority of the proposed algorithm and its flexibility to be used in other studies are clearly demonstrated by analysis.

Design of Programmable Wideband Low Pass Filter with Continuous-Time/Discrete-Time Hybrid Architecture

2017 ◽  
Vol E100.C (10) ◽  
pp. 858-865 ◽  
Author(s):  
Yohei MORISHITA ◽  
Koichi MIZUNO ◽  
Junji SATO ◽  
Koji TAKINAMI ◽  
Kazuaki TAKAHASHI
Keyword(s):  
Discrete Time ◽  
Continuous Time ◽  
Hybrid Architecture ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass
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