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LOW DIMENSIONAL MANIFOLD MODEL FOR IMAGE PROCESSING
∗ STANLEY OSHER † , ZUOQIANG SHI ‡ , AND WEI ZHU §
- In this paper, we propose a novel low dimensional manifold model (LDMM) and apply it to some image processing problems. LDMM is based on the fact that the patch manifolds of many natural images have low dimensional structure. Based on this fact, the dimension of the patch manifold is used as a regularization to recover the image. The key step in LDMM is to solve a Laplace-Beltrami equation over a point cloud which is solved by the point integral method. The point integral method enforces the sample point constraints correctly and gives better results than the standard graph Laplacian. Numerical simulations in image denoising, inpainting and super-resolution problems show that LDMM is a powerful method in image processing.
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First Order Algorithms in Variational Image Processing
- Burger∗ , A. Sawatzky∗ , and G. Steidl† December 16, 2014 1
Introduction: Variational methods in imaging are nowadays developing towards a quite universal and flexible tool, allowing for highly successful approaches on tasks like denoising, deblurring, inpainting, segmentation, super-resolution, disparity, and optical flow estimation. The overall structure of such approaches is of the form
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Parametrized system level design: Real-time XRay image processing case study Conference Paper ·
July 2016 DOI: 10.1109/ASAP.2016.7760793
Abstract—Complex embedded systems are used to facilitate real-time data processing applications. To cope with their surroundings, these systems need to provide some form of parameterization. Developing such systems is challenging as the system needs to execute in a stable and consistent way with the parameterization. In this paper, we want to present how we can develop such a system from C-code using the Compaan technology and show a real-time medical X-Ray image processing case study. Our purpose is to evaluate the system level synthesis flow of Compaan and to assess if the Compaan technology is capable of realizing such complex parametrized system.
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Machine Learning for Plant Phenotyping Needs Image Processing
Keywords: image processing, machine learning, plant phenotyping, stress
We found the article by Singh et al. [1] extremely interesting since it introduces and showcases the utility of machine learning for high throughput data-driven plant phenotyping. With this letter we want to emphasize the role that image analysis and processing have in the phenotyping pipeline beyond what [1] suggests, both in analyzing phenotyping data (e.g., to measure growth) but also when providing effective feature extraction to be used by machine learning. Key recent reviews have shown that it is image analysis itself (what the authors of [1] consider as part of pre-processing) that has brought a renaissance in phenotyping [2]. At the same time, the lack of robust methods to analyze these images is now the new bottleneck [3-5]. And this bottleneck is not easy to overcome. As the following aims to illustrate, it is coupled to the imaging system and the environment but also to the analysis task at hand and requires new skills to help deal with the challenges introduced.
5.Volume 3, Issue 3, March-2016, pp. 119-122 ISSN (O): 2349-7084
Review of Various Image Processing Techniques for Currency Note Authentication
Trisha Chakraborty, Nikita Nalawade, Abhishri Manjre, Akanksha Sarawgi, Pranali P Chaudhari Abstract:- In cash transactions, the biggest challenge faced is counterfeit notes. This problem is only expanding due to the technology available and many fraud cases have been uncovered. Manual detection of counterfeit notes is time consuming and inefficient and hence the need of automated counterfeit detection has raised. To tackle this problem, we studied existing systems using Matlab, which used different methods to detect fake notes
. Keywords – Counterfeit, Image Processing, Matlab, Cloud, Map Reduce
