Image Restoration Using Adaptive Region-Wise p-Norm Filter with Local Constraints

2016 ◽  
Vol 16 (02) ◽  
pp. 1650008
Author(s):  
A. A. Bini ◽  
P. Jidesh
Keyword(s):  
Image Restoration ◽  
Regularization Parameter ◽  
Image Features ◽  
Local Constraints ◽  
Mean Curvature Motion ◽  
Statistical Measures ◽  
Image Characteristics ◽  
Curvature Motion ◽  
Laplacian Filter ◽  
Texture Preservation

In this work, we introduce a feature adaptive second-order p-norm filter with local constraints for image restoration and texture preservation. The p-norm value of the filter is chosen adaptively between 1 and 2 in a local region based on the regional image characteristics. The filter behaves like a mean curvature motion (MCM) [A. Marquina and S. Osher, SIAM Journal of Scientific Computing 22, 387–405 (2000)] in the regions where the p-norm value is 1 and switches to a Laplacian filter in the rest of the regions (where the p-norm value is 2). The proposed study considerably reduces stair-case effect and effectively removes noise from images while deblurring them. The noise is assumed as Gaussian distributed (with zero mean and variance [Formula: see text]) and blur is linearly shift invariant (out-of-focus). The filter converges at a faster rate with semi-implicit Crank–Nicholson scheme. The regularization parameter is initialized and updated based on the local image features and therefore this filter preserves edges, structures, textures and fine details present in images very well. The method is applied on different kinds of images with different image characteristics. We show the response of the filter to various kinds of images and numerically quantify the performance in terms of standard statistical measures.

scholarly journals Image Restoration by Learning Morphological Opening-Closing Network

2020 ◽  
Vol 4 (1) ◽  
pp. 87-107
Author(s):  
Ranjan Mondal ◽  
Moni Shankar Dey ◽  
Bhabatosh Chanda
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
State Of The Art ◽  
Source Code ◽  
Back Propagation ◽  
Image Features ◽  
Main Difficulty ◽  
The Right ◽  
Right Order ◽  
Morphological Opening

AbstractMathematical morphology is a powerful tool for image processing tasks. The main difficulty in designing mathematical morphological algorithm is deciding the order of operators/filters and the corresponding structuring elements (SEs). In this work, we develop morphological network composed of alternate sequences of dilation and erosion layers, which depending on learned SEs, may form opening or closing layers. These layers in the right order along with linear combination (of their outputs) are useful in extracting image features and processing them. Structuring elements in the network are learned by back-propagation method guided by minimization of the loss function. Efficacy of the proposed network is established by applying it to two interesting image restoration problems, namely de-raining and de-hazing. Results are comparable to that of many state-of-the-art algorithms for most of the images. It is also worth mentioning that the number of network parameters to handle is much less than that of popular convolutional neural network for similar tasks. The source code can be found here https://github.com/ranjanZ/Mophological-Opening-Closing-Net

scholarly journals Evaluation and Validation of a Semiautomatic Image Analysis Method: Quantification of Nares Cell Proliferation of Rhinella arenarum (Anura) Detected with the Avidin-Biotin Complex/Peroxidase, DAB

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Javier Eduardo Diaz Zamboni ◽  
Daniela Osella ◽  
Enrique Valentín Paravani ◽  
Víctor Hugo Casco
Keyword(s):  
Gold Standard ◽  
Visual Analysis ◽  
Methodological Approach ◽  
Image Features ◽  
Analysis Method ◽  
New Approach ◽  
Image Characteristics ◽  
Microscopy Image ◽  
Computer Based ◽  
Current Report

The current report presents the development and application of a novel methodological approach for computer-based methods of processing and analysis of proliferative tissues labeled by ABC-peroxidase method using 3, 3′-diaminobenzidine (DAB) as chromogen. This semiautomatic method is proposed to replace the classical manual approach, widely accepted as gold standard. Our method is based on a visual analysis of the microscopy image features from which a computational model is built to generate synthetic images which are used to evaluate and validate the methods of image processing and analysis. The evaluation allows knowing whether the computational methods applied are affected by the change of the image characteristics. Validation allows determining the method’s reliability and analyzing the concordance between the proposed method and a gold standard one. Additional strongness of this new approach is that it may be a framework adaptable to other studies made on any kind of microscopy.

An Adaptive Variational PDE Based Image Restoration Model Using Hopfield Neural Network

2011 ◽  
Vol 48-49 ◽  
pp. 174-178
Author(s):  
Wei Sun ◽  
Sheng Nan Liu
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
Regularization Parameter ◽  
Signal To Noise Ratio ◽  
Hopfield Neural Network ◽  
Adaptive Regularization ◽  
Proposed Model ◽  
Degraded Image ◽  
Restoration Problem ◽  
Restoration Model

An adaptive variational partial differential equation (PDE) based aproach for restoration of gray level images degraded by a known shift-invariant blur function and additive noise is presented. The restoration problem of a degraded image is solved by minimizing this model, and this minimizing problem is realized by using Hopfield neural network. In the proposed image restoration model, an adaptive regularization parameter is developed instead of the constant regularization parameter used in previous PDE model. The value of the adaptive regularization parameter changes according to different regions of the image to remove noises and preserve edge better. Several computer simulation results show that the image restoration results of the proposed model both look better and have better SNR (Signal to Noise Ratio) than the previous variational PDE based model.

The Influence of the Exterior Surface on Grain Boundary Mobility Measurements

2014 ◽  
Vol 95 ◽  
pp. 56-65
Author(s):  
Amy Novick-Cohen ◽  
Anna Zigelman ◽  
Arkady Vilenkin
Keyword(s):  
Grain Boundary ◽  
Grain Boundaries ◽  
Mean Curvature ◽  
Boundary Energy ◽  
Polycrystalline Materials ◽  
Normal Velocity ◽  
Exterior Surface ◽  
Mean Curvature Motion ◽  
Curvature Motion ◽  
Half Loop

Polycrystalline materials typically contain a very large number of grains whose surrounding grain boundaries evolve over time to reducethe overall energy of the microstructure. The evolution of the microstructure is influencedby the motion of the exterior surface since the grain boundaries couple to the exterior surface of the specimen; these effects can be appreciable especially in thin specimens. We model these effects using the classical framework of Mullins, in whichgrain boundaries move by mean curvature motion, Vn =A κ, and the exterior surface evolves by surface diffusion, Vn = -BΔs κ. Here Vn and κ denote the normal velocity and the mean curvature of the respective evolving surfaces, and Δs is the surface Laplacian. A classical way to determine A, the ``reduced mobility," is to make measurements based on the half-loop bicrystalline geometry. In this geometry one of the two grains, which embedded within the other, recedes at a roughly constant rate which can provide an estimate for A. In this note, we report on findings concerning the effects of the exterior surface on grain boundary motion and mobility measurements in the context of the half-loop bicrystalline geometry. We assume that the ratio of grain boundary energy to the exterior surface energy is small, and suitable assumptions are made of the specimen aspect ratio.

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