The most homogeneous dip-scanning method using edgepreserving smoothing for seismic noise attenuation

Highlighting and analyzing the geological features of faults and fractures in seismic data is particularly important for hydrocarbon exploration and exploitation since they are often essential for finding and delineating reservoirs. We apply edge-preserving smoothing （EPS） to seismic processing and propose a most homogeneous dip-scanning method. The method preserves the geological features, eliminate random noise efficiently, obtain dip information, and improve the accuracy of identifying the oil and gas traps.

Contrast Enhancement Based Image Detection Using Edge Preserved Key Pixel Point Filtering

In existing methods for segmented images,either edge point extraction or preservation of edges,compromising contrast images is so sensitive to noise.The Degeneration Threshold Image Detection(DTID)framework has been proposed to improve the contrast of edge filtered images.Initially,DTID uses a Rapid Bilateral Filtering process for filtering edges of contrast images.This filter decomposes input images into base layers in the DTID framework.With minimal filtering time,Rapid Bilateral Filtering handles high dynamic contrast images for smoothening edge preservation.In the DTID framework,Rapid Bilateral Filtering with Shift-Invariant Base Pass Domain Filter is insensitive to noise.This Shift-Invariant Filtering estimates value across edges for removing outliers(i.e.,noise preserving base layers of the contrast image).The intensity values are calculated in the base layer of the contrast image for accurately detecting nearby spatial locations using Shift-Invariant base Pass Domain Filter(SIDF).At last,Affine Planar Transformation is applied to detect edge filtered contrast images in the DTID framework for attaining a high quality of the image.It normalizes the translation and rotation of images.With this,Degeneration Threshold Image Detection maximizes average contrast enhancement quality and performs an experimental evaluation of factors such as detection accuracy,rate,and filtering time on contrast images.Experimental analysis shows that the DTID framework reduces the filtering time taken on contrast images by 54%and improves average contrast enhancement quality by 27%compared to GUMA,HMRF,SWT,and EHS.It provides better performance on the enhancement of average contrast enhancement quality by 28%,detection accuracy rate by 26%,and reduction in filtering time taken on contrast images by 30%compared to state-of-art methods.

A Hybrid Regularization-Based Multi-Frame Super-Resolution Using Bayesian Framework

The prime purpose for the image reconstruction of a multi-frame super-resolution is to reconstruct a higher-resolution image through incorporating the knowledge obtained from a series of relevant low-resolution images,which is useful in numerousfields.Nevertheless,super-resolution image reconstruction methods are usually damaged by undesirable restorative artifacts,which include blurring distortion,noises,and stair-casing effects.Consequently,it is always challenging to achieve balancing between image smoothness and preservation of the edges inside the image.In this research work,we seek to increase the effectiveness of multi-frame super-resolution image reconstruction by increasing the visual information and improving the automated machine perception,which improves human analysis and interpretation processes.Accordingly,we propose a new approach to the image reconstruction of multi-frame super-resolution,so that it is created through the use of the regularization framework.In the proposed approach,the bilateral edge preserving and bilateral total variation regularizations are employed to approximate a high-resolution image generated from a sequence of corresponding images with low-resolution to protect significant features of an image,including sharp image edges and texture details while preventing artifacts.The experimental results of the synthesized image demonstrate that the new proposed approach has improved efficacy both visually and numerically more than other approaches.

Hyperspectral Images-Based Crop Classification Scheme for Agricultural Remote Sensing

Hyperspectral imaging is gaining a significant role in agricultural remote sensing applications.Its data unit is the hyperspectral cube which holds spatial information in two dimensions while spectral band information of each pixel in the third dimension.The classification accuracy of hyperspectral images(HSI)increases significantly by employing both spatial and spectral features.For this work,the data was acquired using an airborne hyperspectral imager system which collected HSI in the visible and near-infrared(VNIR)range of 400 to 1000 nm wavelength within 180 spectral bands.The dataset is collected for nine different crops on agricultural land with a spectral resolution of 3.3 nm wavelength for each pixel.The data was cleaned from geometric distortions and stored with the class labels and annotations of global localization using the inertial navigation system.In this study,a unique pixel-based approach was designed to improve the crops'classification accuracy by using the edge-preserving features(EPF)and principal component analysis(PCA)in conjunction.The preliminary processing generated the high-dimensional EPF stack by applying the edge-preserving filters on acquired HSI.In the second step,this high dimensional stack was treated with the PCA for dimensionality reduction without losing significant spectral information.The resultant feature space(PCA-EPF)demonstrated enhanced class separability for improved crop classification with reduced dimensionality and computational cost.The support vector machines classifier was employed for multiclass classification of target crops using PCA-EPF.The classification performance evaluation was measured in terms of individual class accuracy,overall accuracy,average accuracy,and Cohen kappa factor.The proposed scheme achieved greater than 90%results for all the performance evaluation metrics.The PCA-EPF proved to be an effective attribute for crop classification using hyperspectral imaging in the VNIR range.The proposed scheme is well-suited for practical applications of crops and landfill estimations using agricultural remote sensing methods.

A New Extrapolation Economy Cascadic Multigrid Method for Image Restoration Problems

In this paper, a new extrapolation economy cascadic multigrid method is proposed to solve the image restoration model. The new method combines the new extrapolation formula and quadratic interpolation to design a nonlinear prolongation operator, which provides more accurate initial values for the fine grid level. An edge preserving denoising operator is constructed to remove noise and preserve image edges. The local smoothing operator reduces the influence of staircase effect. The experiment results show that the new method not only improves the computational efficiency but also ensures good recovery quality.

Multiscale Image Dehazing and Restoration:An Application for Visual Surveillance

The captured outdoor images and videos may appear blurred due to haze,fog,and bad weather conditions.Water droplets or dust particles in the atmosphere cause the light to scatter,resulting in very limited scene discernibility and deterioration in the quality of the image captured.Currently,image dehazing has gainedmuch popularity because of its usability in a wide variety of applications.Various algorithms have been proposed to solve this ill-posed problem.These algorithms provide quite promising results in some cases,but they include undesirable artifacts and noise in haze patches in adverse cases.Some of these techniques take unrealistic processing time for high image resolution.In this paper,to achieve real-time halo-free dehazing,fast and effective single image dehazing we propose a simple but effective image restoration technique using multiple patches.It will improve the shortcomings of DCP and improve its speed and efficiency for high-resolution images.A coarse transmissionmap is estimated by using the minimumof different size patches.Then a cascaded fast guided filter is used to refine the transmission map.We introduce an efficient scaling technique for transmission map estimation,which gives an advantage of very low-performance degradation for a highresolution image.For performance evaluation,quantitative,qualitative and computational time comparisons have been performed,which provide quiet faithful results in speed,quality,and reliability of handling bright surfaces.

A Bayesian Super Resolution Algorithm Based on Synthetic Gradient Distribution

A novel Bayesian super resolution (SR) algorithm based on the distribution of synthetic gradient is proposed. The synthetic gradient combines prior information in horizontal, vertical, and diagonal directions. Its distribution is modeled as a Lorentzian function and regarded as a new image model which can sufficiently regularize the ill-posed algorithm and preserve the edges in the reconstructed images. The graduated nonconvexity (GNC) optimization is employed to guarantee the convergence of the proposed Lorentzian SR (LSR) algorithm to the global minimum. The performance of LSR is compared with conventional algorithms, and experimental results demonstrate that the proposed algorithm obtains both subjective and objective gains.

Improved image filter based on SPCNN

By extraction of the thoughts of non-linear model and adaptive model match, an improved Nagao filter is brought. Meanwhile a technique based on simplified pulse coupled neural network and used for noise positioning, is put forward. Combining the two methods above, we acquire a new method that can restore images corrupted by salt and pepper noise. Experiments show that this method is more preferable than other popular ones, and still works well while noise density fluctuates severely.

A maximum a posteriori super resolution algorithm based on multidimensional Lorentzian distribution

This paper presents a threshold-free maximum a posteriori (MAP) super resolution (SR) algorithm to reconstruct high resolution (HR) images with sharp edges. The joint distribution of directional edge images is modeled as a multidimensional Lorentzian (MDL) function and regarded as a new image prior. This model makes full use of gradient information to restrict the solution space and yields an edge-preserving SR algorithm. The Lorentzian parameters in the cost function are replaced with a tunable variable, and graduated nonconvexity (GNC) optimization is used to guarantee that the proposed multidimensional Lorentzian SR (MDLSR) algorithm converges to the global minimum. Simulation results show the effectiveness of the MDLSR algorithm as well as its superiority over conventional SR methods.