Vision-based closed-loop control of mobile microrobots for microhandling tasks

Buerkle, Axel; Schmoeckel, F.; Kiefer, Matthias; Amavasai, B.P.; Caparrelli, Fabio; Selvan, Arul; Travis, J.R.

doi:10.1117/12.444125

Cited by 27 publications

(11 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For quantitative comparison, the well known benchmark image sharpness measure; the Tenegrad (TEN) is used. This measure is based on gradient magnitude maximization and is considered one of the most robust and functionality accurate image quality measures [13]. The original test images and the enhanced images using unsharp masking and the proposed technique are shown in Figure 4.…”

Section: Resultsmentioning

confidence: 99%

A modified unsharp-masking technique for image contrast enhancement

Jafar

Darabkh

2011

Eighth International Multi-Conference on Systems, Signals &Amp; Devices

View full text Add to dashboard Cite

Unsharp masking is a popular and simple technique for contrast enhancement and sharpening in digital images. The basic idea in this technique is to emphasize edges and discontinuities in the image by adding the edge information back to the original image. In order to achieve higher contrast level in the processed image, the edge information can be scaled prior addition to the original image. However, there are no guidelines on how to specify the scaling values. In this paper, we propose a novel technique to scale the edge information automatically. The underlying principle in the proposed technique is to apply the well-known histogram equalization technique on the edge histogram of the image. Equalizing the edge histogram is performed on selected regions of the edge histogram in order to reduce noise amplification in smooth regions and edge ringing artifacts. The proposed technique has been tested on a large set of images qualitatively and quantitatively and is proven to produce satisfactory results.

show abstract

Section: Resultsmentioning

confidence: 99%

A modified unsharp-masking technique for image contrast enhancement

Jafar

Darabkh

2011

Eighth International Multi-Conference on Systems, Signals &Amp; Devices

View full text Add to dashboard Cite

show abstract

“…Three metrics are used to quantify the performance: 1) the average distance between pixels on the grayscale (PixDist), 2) the Tenengrad value [2,3], and 3) the root mean squared distortion error (RMSE). The PixDist and Tenengrad values measure contrast and edge enhancement, respectively.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Automatic contrast enhancement by variational minimax optimization

Wang

2015

2015 IEEE International Conference on Image Processing (ICIP)

View full text Add to dashboard Cite

We present an automatic global contrast enhancement method using variational optimization. A novel energy functional is formulated as a convex combination of histogram equalization and quadratic distortion constraint regularization. The desired image gray-level transformation is obtained through minimax optimization. This method sets a generic framework for developing distortion regularized histogram equalization techniques for global or spatially adaptive contrast enhancement. We demonstrate the capability of our method using various types of low contrast images by visual inspection and quantitative comparison.

show abstract

“…An IQM M(r), where vector r = {x,y} denotes the spatial coordinates, is defined as the convolution result between a spatially-varying image quality metric J(r) [23][24][25] and a Gaussian kernel G(r,a):…”

Section: 1mentioning

confidence: 99%

Automated video enhancement from a stream of atmospherically-distorted images: the lucky-region fusion approach

et al. 2009

View full text Add to dashboard Cite

An automated video enhancement technique capable of image fusion from a stream of randomly-distorted images of a still scene is presented in this paper. The technique is based on the "lucky-region" fusion (LRF) approach and aims to improve locally the image quality according to the following steps: (1) for each image of the video stream an image quality map (IQM) which characterizes locally the image quality is computed, (2) each IQM is compared to that of the current fused image leading to the selection of best quality regions (the "lucky-regions"), and (3) the selected regions are merged into the fused video stream. While the LRF approach succeeds in producing images with significantly improved image quality compared to the source images, its performance depends on the imaging conditions and requires adjustment of its fusion parameter -the fusion kernel size -in order to adapt to an evolving environment (e.g. a turbulent atmosphere). Parameter selection was so far performed manually using a trial-and-error approach which causes the technique to be impractical for a real world implementation. The automated LRF technique presented is relaxed from this requirement and selects automatically the fusion parameter based on the analysis of the source images making it more suitable for practical systems. The improved LRF technique is applied to imaging through atmospheric turbulence for various imaging conditions and scenes of interest. In each case automatically-fused video streams demonstrate increases in image quality comparable to that obtained with manual selection of the fusion parameter.

show abstract

Vision-based closed-loop control of mobile microrobots for microhandling tasks

Cited by 27 publications

References 12 publications

A modified unsharp-masking technique for image contrast enhancement

A modified unsharp-masking technique for image contrast enhancement

Automatic contrast enhancement by variational minimax optimization

Automated video enhancement from a stream of atmospherically-distorted images: the lucky-region fusion approach

Contact Info

Product

Resources

About