Joint inversion of gravity and vertical gradient data based on modified structural similarity index for the structural and petrophysical consistency constraint

Joint inversion is one of the most effective methods for reducing non-uniqueness for geophysical inversion.The current joint inversion methods can be divided into the structural consistency constraint and petrophysical consistency constraint methods,which are mutually independent.Currently,there is a need for joint inversion methods that can comprehensively consider the structural consistency constraints and petrophysical consistency constraints.This paper develops the structural similarity index(SSIM)as a new structural and petrophysical consistency constraint for the joint inversion of gravity and vertical gradient data.The SSIM constraint is in the form of a fraction,which may have analytical singularities.Therefore,converting the fractional form to the subtractive form can solve the problem of analytic singularity and finally form a modified structural consistency index of the joint inversion,which enhances the stability of the SSIM constraint applied to the joint inversion.Compared to the reconstructed results from the cross-gradient inversion,the proposed method presents good performance and stability.The SSIM algorithm is a new joint inversion method for petrophysical and structural constraints.It can promote the consistency of the recovered models from the distribution and the structure of the physical property values.Then,applications to synthetic data illustrate that the algorithm proposed in this paper can well process the synthetic data and acquire good reconstructed results.

Underwater Image Enhancement Using MIRNet

In recent years,enhancement of underwater images is a challenging task,which is gaining priority since the human eye cannot perceive images under water.The significant details underwater are not clearly captured using the conventional image acquisition techniques,and also they are expensive.Hence,the quality of the image processing algorithms can be enhanced in the absence of costly and reliable acquisition techniques.Traditional algorithms have certain limitations in the case of these images with varying degrees of fuzziness and color deviation.In the proposed model,the authors used a deep learning model for underwater image enhancement.First,the original image is pre-processed by the white balance algorithm for colour correction and the contrast of the image is improved using the contrast enhancement technique.Next,the pre-processed image is given to the MIRNet for enhancement.MIRNet is a deep learning framework that can be used to enhance the low-light level images.The enhanced image quality is measured using peak signal-to-noise ratio(PSNR),root mean square error(RMSE),and structural similarity index(SSIM)parameters.

Denoising Letter Images from Scanned Invoices Using Stacked Autoencoders

Invoice document digitization is crucial for efficient management in industries.The scanned invoice image is often noisy due to various reasons.This affects the OCR(optical character recognition)detection accuracy.In this paper,letter data obtained from images of invoices are denoised using a modified autoencoder based deep learning method.A stacked denoising autoencoder(SDAE)is implemented with two hidden layers each in encoder network and decoder network.In order to capture the most salient features of training samples,a undercomplete autoencoder is designed with non-linear encoder and decoder function.This autoencoder is regularized for denoising application using a combined loss function which considers both mean square error and binary cross entropy.A dataset consisting of 59,119 letter images,which contains both English alphabets(upper and lower case)and numbers(0 to 9)is prepared from many scanned invoices images and windows true type(.ttf)files,are used for training the neural network.Performance is analyzed in terms of Signal to Noise Ratio(SNR),Peak Signal to Noise Ratio(PSNR),Structural Similarity Index(SSIM)and Universal Image Quality Index(UQI)and compared with other filtering techniques like Nonlocal Means filter,Anisotropic diffusion filter,Gaussian filters and Mean filters.Denoising performance of proposed SDAE is compared with existing SDAE with single loss function in terms of SNR and PSNR values.Results show the superior performance of proposed SDAE method.

Fully automatic identification and discrimination of sperm’s parts in microscopic images of stained human semen smear

In the last years, digital image processing and analysis are used for computer assisted evaluation of semen quality with therapeutic goals or to estimate its fertility by means of spermatozoid motility and morphology. Sperm morphology is assessed routinely as part of standard laboratory analysis in the diagnosis of human male infertility. Nowadays assessments of sperm morphology are mostly done based on subjective criteria. In order to avoid subjectivity, numerous studies that incorporate image analysis techniques in the assessment of sperm morphology have been proposed. The primary step of all these methods is segmentation of sperm’s parts. In this paper, we have proposed a new method for segmentation of sperm’s Acrosome, Nucleus, Mid-piece and identification of sperm’s tail through some points which are placed on the sperm’s tail, accurately. These estimated points could be used to verify the morphological characteristics of sperm’s tail such as length, shape and etc. At first, sperm’s Acrosome, Nucleus and Mid-piece are segmented through a method based on a Bayesian classifier which utilizes the entropy based expectation–maximization (EM) algorithm and Markov random field (MRF) model to obtain and upgrade the class conditional probability density function (CCPDF) and the apriori probability of each class. Then, a pixel at the end of sperm’s Mid-piece, is selected as an initial point. To find other pixels which are placed on the sperm’s tail, structural similarity index (SSIM) is used in an iterative scheme. In order to stop the algorithm automatically at the end of sperm’s tail, local entropy is estimated and used as a feature to determine if a point is located on the sperm’s tail or not. To compare the performance of the proposed approach with those of previous approaches including manual segmentation, the Accuracy, Sensitivity and Specificity were calculated.

A Hybrid Security Framework for Medical Image Communication

Authentication of the digital image has much attention for the digital revolution.Digital image authentication can be verified with image watermarking and image encryption schemes.These schemes are widely used to protect images against forgery attacks,and they are useful for protecting copyright and rightful ownership.Depending on the desirable applications,several image encryption and watermarking schemes have been proposed to moderate this attention.This framework presents a new scheme that combines a Walsh Hadamard Transform(WHT)-based image watermarking scheme with an image encryption scheme based on Double Random Phase Encoding(DRPE).First,on the sender side,the secret medical image is encrypted using DRPE.Then the encrypted image is watermarking based on WHT.The combination between watermarking and encryption increases the security and robustness of transmitting an image.The performance evaluation of the proposed scheme is obtained by testing Structural Similarity Index(SSIM),Peak Signal-to-Noise Ratio(PSNR),Normalized cross-correlation(NC),and Feature Similarity Index(FSIM).

Truncation and Rounding-Based Scalable Approximate Multiplier Design for Computer Imaging Applications

Advanced technology used for arithmetic computing application,comprises greater number of approximatemultipliers and approximate adders.Truncation and Rounding-based Scalable ApproximateMultiplier(TRSAM)distinguish a variety of modes based on height(h)and truncation(t)as TRSAM(h,t)in the architecture.This TRSAM operation produces higher absolute error in Least Significant Bit(LSB)data shift unit.A new scalable approximate multiplier approach that uses truncation and rounding TRSAM(3,7)is proposed to increase themultiplier accuracy.With the help of foremost one bit architecture,the proposed scalable approximate multiplier approach reduces the partial products.The proposed approximate TRSAM multiplier architecture gives better results in terms of area,delay,and power.The accuracy of 95.2%and the energy utilization of 24.6 nJ is observed in the proposed multiplier design.The proposed approach shows 0.11%,0.23%,and 0.24%less Mean Absolute Relative Error(MARE)when compared with the existing approach for the input of 8-bit,16-bit,and 32-bit respectively.It also shows 0.13%,0.19%,and 0.2%less Variance of Absolute Relative Error(VARE)when compared with the existing approach for the input of 8-bit,16-bit,and 32-bit respectively.The proposed approach is implemented with Field-Programmable Gate Array(FPGA)and shows the delay of 3.640,6.481,12.505,22.572,and 36.893 ns for the input of 8-bit,16-bit,32-bit,64-bit,and 128-bit respectively.The proposed approach is applied in digital filters designwhich shows the Peak-Signal-to-NoiseRatio(PSNR)of 25.05 dB and Structural Similarity Index Measure(SSIM)of 0.98 with 393 pJ energy consumptions when used in image application.The proposed approach is simulated with Xilinx and MATLAB and implemented with FPGA.

Performance Analysis of Image Smoothing Techniques on a New Fractional Convolution Mask for Image Edge Detection

We present the analysis of three independent and most widely used image smoothing techniques on a new fractional based convolution edge detector originally constructed by same authors for image edge analysis. The implementation was done using only Gaussian function as its smoothing function based on predefined assumptions and therefore did not scale well for some types of edges and noise. The experiments conducted on this mask using known images with realistic geometry suggested the need for image smoothing adaptation to obtain a more optimal performance. In this paper, we use the structural similarity index measure and show that the adaptation technique for choosing smoothing function has significant advantages over a single function implementation. The new adaptive fractional based convolution mask can smoothly find edges of various types in detail quite significantly. The method can now trap both local discontinuities in intensity and its derivatives as well as locating Dirac edges.

A video structural similarity quality metric based on a joint spatial-temporal visual attention model

Objective video quality assessment plays a very important role in multimedia signal processing. Several extensions of the structural similarity （SSIM） index could not predict the quality of the video sequence effectively. In this paper we propose a structural similarity quality metric for videos based on a spatial-temporal visual attention model. This model acquires the motion attended region and the distortion attended region by computing the motion features and the distortion contrast. It mimics the visual attention shifting between the two attended regions and takes the burst of error into account by introducing the non-linear weighting fimctions to give a much higher weighting factor to the extremely damaged frames. The proposed metric based on the model renders the final object quality rating of the whole video sequence and is validated using the 50 Hz video sequences of Video Quality Experts Group Phase I test database.

Spatially Adaptive Image Restoration Using Fuzzy Punctual Kriging

We present a general formulation based on punctual kriging and fuzzy concepts for image restoration in spatial domain. Gray-level images degraded with Gaussian white noise have been considered. Based on the pixel local neighborhood, fuzzy logic has been employed intelligently to avoid unnecessary estimation of a pixel. The intensity estimation of the selected pixels is then carried out by employing punctual kriging in conjunction with the method of Lagrange multipliers and estimates of local semi-variances. Application of such a hybrid technique performing both selection and intensity estimation of a pixel demonstrates substantial improvement in the image quality as compared to the adaptive Wiener filter and existing fuzzykriging approaches. It has been found that these filters achieve noise reduction without loss of structural detail information, as indicated by their higher structure similarity indices, peak signal to noise ratios and the new variogram based quality measures.

A combination of learning and non-learning based method for enhancement, compression and reconstruction of underwater images

Complex nature of underwater environment poses biggest challenge towards image acquisition and transmission of underwater images.This paper proposes an integrated approach which consists of a non-learning enhancement method with deep Convolutional Neural Networks(CNN)for compression and reconstruction of the image.The proposed method does color and contrast correction for image enhancement.The enhanced images are down-sampled using 9-layer CNN followed by Discrete Wavelet Transform(DWT).The decompression is done by using Inverse DWT.Further,the sub-pixel up-sampled image is de-blurred using a three-layer CNN.Residual Dense CNN(RD-CNN)is used to improve the quality of the reconstructed image after deblurring.The quality of the reconstructed images is measured using Peak Signal to Noise Ratio(PSNR)and Structural Similarity Index Metric(SSIM).The proposed model provides better image enhancement,compression,and reconstruction quality than the existing state-of-the-art methods and Super Resolution CNN(SRCNN)respectively.