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This article is from Beilstein Journal of Nanotechnology , volume 3 . Abstract Modern high-speed atomic force microscopes generate significant quantities of data in a short amount of time. Each image in the sequence has to be processed quickly and accurately in order to obtain a true representation of the sample and its changes over time. This paper presents an automated, adaptive algorithm for the required processing of AFM images. The algorithm adaptively corrects for both common one-dimensional distortions as well as the most common two-dimensional distortions. This method uses an iterative thresholded processing algorithm for rapid and accurate separation of background and surface topography. This separation prevents artificial bias from topographic features and ensures the best possible coherence between the different images in a sequence. This method is equally applicable to all channels of AFM data, and can process images in seconds.

This article is from PLoS ONE , volume 9 . Abstract Objective: To assess the advantages of Adaptive Iterative Dose Reduction using Three Dimensional Processing (AIDR3D) for image quality improvement and dose reduction for chest computed tomography (CT). Methods: Institutional Review Boards approved this study and informed consent was obtained. Eighty-eight subjects underwent chest CT at five institutions using identical scanners and protocols. During a single visit, each subject was scanned using different tube currents: 240, 120, and 60 mA. Scan data were converted to images using AIDR3D and a conventional reconstruction mode (without AIDR3D). Using a 5-point scale from 1 (non-diagnostic) to 5 (excellent), three blinded observers independently evaluated image quality for three lung zones, four patterns of lung disease (nodule/mass, emphysema, bronchiolitis, and diffuse lung disease), and three mediastinal measurements (small structure visibility, streak artifacts, and shoulder artifacts). Differences in these scores were assessed by Scheffe's test. Results: At each tube current, scans using AIDR3D had higher scores than those without AIDR3D, which were significant for lung zones (p

Variable exponent spaces have found interesting applications in real world problems. Recently, there have been considerable interest in utilizing variational and evolution problems based on variable exponents for imaging applications. The main classes of partial differential equations (PDEs) related to the variable exponents involve the $p(\cdot)$-Laplacian. In imaging applications, the variable exponent can approach the critical value $1$, and this poses unique challenges in proving existence of solutions, which have not been mastered earlier. In this work, we develop some additional functional framework to study the time-dependent parabolic flows with critical variable exponents. Specifically, we consider bounded vectorial partial variation (BVPV) space and its variable exponent counterpart. We prove the existence of weak solutions of critical vectorial $p(t,x)$-Laplacian flow in our variable exponent space. For non-time-dependent variable exponent based critical vectorial $p(x)$-Laplacian flow we obtain a semigroup solution. The results are new even in the scalar case. This is a theory-oriented draft, and the full paper will provide detailed experimental results on color image restoration using various example for the variable exponents and compare them traditional PDE based image processing procedures. Our results will indicate the applicability of variable exponent Laplacian flows in image processing in general and image restoration in particular.

In recent years, significant progress has been made towards achieving autonomous roadway navigation using video images. None of these systems take full advantage of all the information in the 512x512 pixel frame, 30 frame/ second color image sequence. This can be attributed to the large amount of data which is present in the color video image stream (22.5 Mbytes/sec) as well as the limited amount of computing resources available to the systems. We have increased the available computing power by using a parallel computer. Specifically, a single instruction, multiple data (SIMD) machine was used to develop simple and efficient parallel algorithms, largely based on connectionist techniques, which can process every pixel in the incoming 30 frame/second, color video image stream. The system uses substantially larger frames and processes them at faster rates than other color road following systems. This is achievable through the use of algorithms specifically designed for a fine-grained parallel machine as opposed to ones ported from existing systems to parallel architectures. The algorithms presented here were tested on 4k and 16k processor MasPar MP-1 and on 4K, 8K, and 16K processor MasPar MP-2 parallel machines and were used to drive Carnegie Mellon's testbed vehicle, the Navlab I, on paved roads near campus.

In recent years, significant progress has been made towards achieving autonomous roadway navigation using video images. None of these systems take full advantage of all the information in the 512x512 pixel frame, 30 frame/ second color image sequence. This can be attributed to the large amount of data which is present in the color video image stream (22.5 Mbytes/sec) as well as the limited amount of computing resources available to the systems. We have increased the available computing power by using a parallel computer. Specifically, a single instruction, multiple data (SIMD) machine was used to develop simple and efficient parallel algorithms, largely based on connectionist techniques, which can process every pixel in the incoming 30 frame/second, color video image stream. The system uses substantially larger frames and processes them at faster rates than other color road following systems. This is achievable through the use of algorithms specifically designed for a fine-grained parallel machine as opposed to ones ported from existing systems to parallel architectures. The algorithms presented here were tested on 4k and 16k processor MasPar MP-1 and on 4K, 8K, and 16K processor MasPar MP-2 parallel machines and were used to drive Carnegie Mellon's testbed vehicle, the Navlab I, on paved roads near campus.

The preliminary design of the Information Adaptive System is presented. The role of the microprocessor in the implementation of the individual processing elements is discussed. Particular emphasis is placed on multispectral image data processing.

The non-stationary nature of image characteristics calls for adaptive processing, based on the local image content. We propose a simple and flexible method to learn local tuning of parameters in adaptive image processing: we extract simple local features from an image and learn the relation between these features and the optimal filtering parameters. Learning is performed by optimizing a user defined cost function (any image quality metric) on a training set. We apply our method to three classical problems (denoising, demosaicing and deblurring) and we show the effectiveness of the learned parameter modulation strategies. We also show that these strategies are consistent with theoretical results from the literature.

This thesis has two parts, both related to the development of smart sensor systems. The first part is a theoretical development of two families of adaptive spatial filters for suppressing background clutters in infrared images and based on the minimization of mean- squared error or the maximization of signal to noise ratio criterion. Seven different nonlinear search techniques have been developed for the adaptation process. They have been applied to two real world infrared test images and exhibit fast convergence rate with no misadjustment . The second part is an experimental development of a multiple microcomputer system which can be a candidate for an on-board processor system. A multiple star, multiple cluster architecture was developed whose intercommunication is managed by a three level control including central controller, distributed controller and random priority controller. The adaptive spatial filter has been successfully implemented on this system using partitioning for parallel computing.

This thesis has two parts, both related to the development of smart sensor systems. The first part is a theoretical development of two families of adaptive spatial filters for suppressing background clutters in infrared images and based on the minimization of mean- squared error or the maximization of signal to noise ratio criterion. Seven different nonlinear search techniques have been developed for the adaptation process. They have been applied to two real world infrared test images and exhibit fast convergence rate with no misadjustment . The second part is an experimental development of a multiple microcomputer system which can be a candidate for an on-board processor system. A multiple star, multiple cluster architecture was developed whose intercommunication is managed by a three level control including central controller, distributed controller and random priority controller. The adaptive spatial filter has been successfully implemented on this system using partitioning for parallel computing.

Thesis (Ph. D.)--Naval Postgraduate School, 1980

Because of the stochastic and nonstationary nature of image processes, an adaptive estimation algorithm is proposed and evaluated for on-line filtering of an image scanned in a raster pattern. This algorithm combines a least squares parameter identification procedure with a two-dimensional reduced update Kalman filter. Results using an image with a 3 dB signal to noise ratio indicate that this adaptive algorithm is very effective for image restoration. (Author)

Because of the stochastic and nonstationary nature of image processes, an adaptive estimation algorithm is proposed and evaluated for on-line filtering of an image scanned in a raster pattern. This algorithm combines a least squares parameter identification procedure with a two-dimensional reduced update Kalman filter. Results using an image with a 3 dB signal to noise ratio indicate that this adaptive algorithm is very effective for image restoration. (Author)

This was an unusual grant in that it s purpose was to improve ongoing basic research and make it more efficient and effective, rather than to actually sponsor research itself. We are pleased to report that this was being achieved. Activity under this grant is resulting in more effective adaptive optics and image post-processing basic research for the Air Force. In particular, it is enabling cross-organizational communication to a larger extent than had been previously underway, and it has fostered hundreds of interactions that would not have occurred otherwise. It is also strengthening the collegial interchanges and scientific cooperation between Air Force Research Laboratory (AFRL) in-house researchers, on-site contractors, university scientists, and industry (primarily small businesses).

In passive detection of small infrared targets in image data, we are faced with difficult task of enhancing some characteristic of the target or signal while suppressing the clutter or background image noise. We reported that an effective means by which targets may be identified is to exploit characteristics which exists between scenes measured in different bands in the Long Wave Infrared (LWIR) region of the electromagnetic (EM) spectrum. These methods are broadly termed multispectral techniques. In this paper we present a method by which in two-dimensional least-mean square (LMS) adaptive filter is used to distinguish between target and clutter using multispectral techniques.

This thesis has two parts, both related to the development of smart sensor systems. The first part is a theoretical development of two families of adaptive spatial filters for suppressing background clutters in infrared images and based on the minimization of mean squared error or the maximization of signal to noise ratio criterion. Seven different nonlinear search techniques have been developed for the adaptation process. They have been applied to two real world infrared test images and exhibit fast convergence rate with no misadjustment. The second part is an experimental development of a multiple microcomputer system which can be a candidate for an on-board processor system. A multiple star, multiple cluster architecture was developed whose intercommunication is managed by a three-level control including central controller, distributed controller and random priority controller. The adaptive spatial filter has been successfully implemented on this system using partitioning for parallel computing. (Author)

This report states the work performed by the Cameron project. The goal of the Cameron project is to make FPGAs and other adaptive computer systems available to more applications programmers, by raising the abstraction level from hardware circuits to software algorithms. To this end, we have developed a variant of the C programming language and an optimizing compiler that maps high-level programs directly onto FPGAs, and have tested the language and compiler on a variety of image processing (and other) applications. A high level language with its one step compiler for Adaptive Computing Systems has been designed and implemented, and the performance of DoD relevant applications has been analyzed on commercial off-the-shelf reconfigurable hardware.

Learning an Adaptive Model for Extreme Low-light Raw Image Processing 作者： 付清旭 1 遆晓光 1 张雨1 1 作者单位： 1. 哈尔滨工业大学 通讯作者： 遆晓光 提交时间： 2020-04-14 摘要: Low-light images suffer from severe noise and low illumination. Current deep learning models that are trained with real-world images have excellent noise reduction, but a ratio parameter must be chosen manually to complete the enhancement pipeline. In this work, we propose an adaptive low-light raw image enhancement network to avoid parameter-handcrafting and to improve image quality. The proposed method can be divided into two sub-models: Brightness Prediction (BP) and Exposure Shifting (ES). The former is designed to control the brightness of the resulting image by estimating a guideline exposure time t 1 . The latter learns to approximate an exposure-shifting operator ES, converting a low-light image with real exposure time t 0 to a noise-free image with guideline exposure time t 1 . Additionally, structural similarity (SSIM) loss and Image Enhancement Vector (IEV) are introduced to promote image quality, and a new Campus Image Dataset (CID) is proposed to overcome the limitations of the existing datasets and to supervise the training of the proposed model. In quantitative tests, it is shown that the proposed method has the lowest Noise Level Estimation (NLE) score compared with BM3D-based low-light algorithms, suggesting a superior denoising performance. Furthermore, those tests illustrate that the proposed method is able to adaptively control the global image brightness according to the content of the image scene. Lastly, the potential application in video processing is briefly discussed. Abstract: Low-light images suffer from severe noise and low illumination. Current deep learning models that are trained with real-world images have excellent noise reduction, but a ratio parameter must be chosen manually to complete the enhancement pipeline. In this work, we propose an adaptive low-light raw image enhancement network to avoid parameter-handcrafting and to improve image quality. The proposed method can be divided into two sub-models: Brightness Prediction (BP) and Exposure Shifting (ES). The former is designed to control the brightness of the resulting image by estimating a guideline exposure time t 1 . The latter learns to approximate an exposure-shifting operator ES, converting a low-light image with real exposure time t 0 to a noise-free image with guideline exposure time t 1 . Additionally, structural similarity (SSIM) loss and Image Enhancement Vector (IEV) are introduced to promote image quality, and a new Campus Image Dataset (CID) is proposed to overcome the limitations of the existing datasets and to supervise the training of the proposed model. In quantitative tests, it is shown that the proposed method has the lowest Noise Level Estimation (NLE) score compared with BM3D-based low-light algorithms, suggesting a superior denoising performance. Furthermore, those tests illustrate that the proposed method is able to adaptively control the global image brightness according to the content of the image scene. Lastly, the potential application in video processing is briefly discussed. " " Learning an Adaptive Model Low-light Raw Image Image Enhancement 来自： 张雨 分类： 计算机科学 >> 计算机应用技术 引用： ChinaXiv:202004.00026 (或此版本 ChinaXiv:202004.00026V1 ) doi:10.12074/202004.00026 CSTR:32003.36.ChinaXiv.202004.00026.V1 推荐引用方式： 付清旭,遆晓光,张雨1.(2020).Learning an Adaptive Model for Extreme Low-light Raw Image Processing.中国科学院科技论文预发布平台.[ChinaXiv:202004.00026] 版本历史 [V1] 2020-04-14 17:54:47 ChinaXiv:202004.00026V1 下载全文

A one-dimensional (1-D) approach to the problem of adaptive image restoration is presented. In this approach, we use a cascade of four 1-D adaptive filters oriented in the four major correlation directions of the image, with each filter treating the image as a 1-D signal. The objective of this 1-D approach is to improve the performance of the more general two-dimensional (2-D) approach. This differs considerably from previous 1-D approaches, the objectives of which have typically been to approximate a more general 2-D approach for computational reasons and not to improve its performance. The main advantage of this new 1-D approach is its capability to preserve edges in the image while removing noise in all regions of the image, including the edge regions. To illustrate this point, the approach is applied to existing 2-D image restoration algorithms. Experimental results with images degraded by additive white noise at various SNRs (signal to noise ratios) are presented. Further examples illustrate the application of 1-D restoration techniques based on this approach to images degraded by blurring and additive white noise and images degraded by multiplicative noise. Another example shows its usefulness in the reduction of quantization noise in pulse code modulation image coding.