Higher Peak Signal To Noise Ratio Research Articles

Enhancement of mammogram images by using entropy improvement approach

Breast cancer in women has been the most often diagnosed cancer. Digital mammogram becomes the most effective imaging method to detect breast cancer in early stage. In breast cancer screening, radiologists typically miss about 10–30% of tumors due to the speculated margins of lesions. Mammogram is a low contrast image whose quality needs to be improved for better interpretation. The performance of the validation of pre-processing methods for mammogram image enhancement is done by performance metrics such as peak signal to noise ratio (PSNR) and mean square error (MSE). Good filtering technique having higher PSNR and low MSE value. Experimental results on the digital database for screening mammography images shown that the non-local mean filter is better for mammogram image enhancement. Further we proposed mammogram images enhancement by entropy improvement method by considering non-local filtered images. These methodologies could add to the effective discovery of masses and micro calcifications in mammograms.

Image Interpolation Using a Rational Bi-Cubic Ball

This study deals with the application of new rational bi-cubic Ball function with six parameters in image interpolation, especially for the grayscale image. These six free parameters can be modified to get better and quality image resolution, and refine the shape of the interpolating surface. This bivariate rational Ball function has been extended from univariate cases by using a tensor product approach. The proposed scheme is tested for image upscaling with factors of two and four through an efficient algorithm. The effectiveness of the proposed scheme is measured by using an image quality assessment (IQA), such as peak-signal-to-noise-ratio (PSNR), root mean square error (RMSE) or feature similarity (FSIM) index. Numerical and graphical results with comparisons against some existing scheme are presented by using MATLAB. The proposed scheme resulted in higher PSNR and FSIM, and smaller RMSE. Thus, the new rational bi-cubic Ball with six parameters is better than the existing scheme via an efficient algorithm.

Rational bicubic Ball for image interpolation

In this study, a new rational bicubic Ball with six free parameters is proposed for image interpolation. These free parameters can be used to refine the shape of the surface. This rational bicubic Ball function is constructed by using tensor product approach. The proposed scheme is tested to grayscale image upscale. An efficient algorithm is employed to upscales the original image with factor 2 and 4. The effectiveness of proposed scheme is measured in terms of Peak Signal-to-Noise Ratio (PSNR), Root Mean Square Error (RMSE) and Feature SIMilarity (FSIM) index including comparison with some existing schemes. Numerical and graphical results for image interpolation are presented by using MATLAB. We found out the proposed scheme resulting with higher PSNR and FSIM and smaller in RMSE. Thus, the new rational bicubic Ball with six parameters is better than the existing schemes.

De-Noising of MRI Brain Tumor Image using Deep Convolutional Neural Network

Medical images must be introduced to specialists or doctors with high precision for the diagnosis of critical diseases like a brain tumor. Presently, a lot of research is going on in the area of image denoising and found to be an emerging one. In this paper, a pretrained feed-forward denoising using convolutional neural networks (DnCNNs) is considered and found to be better than conventional filters used. The results are compared with the Gaussian, adaptive, bilateral and guided filters to reduce the noise from the image. Different noises like Salt and pepper, Gaussian, Poisson, and Speckle noise with variance of 0.05 are used to corrupt the input image and result in the noisy image. Performance metrics Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index of Image (SSIM) of the denoised image are calculated and compared across all filters and noises. The proposed DnCNN method has high PSNR and SSIM for all noises in terms of result performance.

Coded aperture snapshot linear-Stokes imaging spectropolarimeter

In this paper, we present a coded aperture snapshot linear-Stokes imaging spectropolarimeter (CASSIS) based on a combination of coded aperture spectral imager and a division-of-focal-plane polarization camera. The CASSIS achieves aperture coding with a digital micromirror device (DMD) first, and then encodes polarization state using the polarization camera. Finally, the camera gets the coded polarization spatio-spectral image at a single snapshot. Linear-Stokes spectral images are estimated with sparsity-constrained optimization from the acquired image. The principle and feasibility are validated with simulations. It is noted that a modest peak-signal-to-noise ratio (PSNR) is recovered with a single snapshot, and higher PSNR is obtained with multiple snapshots.

Comparative Analysis of SVD and Progressive SPIHT techniques for Compression of MRI and CT Images

MRI and CT images generated by medical imaging techniques require different compression techniques with considerable image quality by reducing the storage space. This paper presents the performances two compression techniques for images i.e. Singular Value Decomposition (SVD) using singular values and wavelet transform based progressive structure of Set Partitioning In Hierarchical Trees (SPIHT). These two techniques are practiced on MRI and CT images of brain and the results of two techniques are compared. SVD with less singular value provides high Compression Ratio (CR) with less Peak Signal to Noise Ratio (PSNR), where as multi resolution based SPIHT technique provides more PSNR with better CR. These two techniques are compared with the quality metrics of PSNR, Mean Squared Error (MSE), CR and Bit Per Pixels (BPP). From the results, Wavelet based progressive SPIHT technique provides high PSNR, low MSE with better CR compared to SVD technique.

Optimasi Keamanan Watermarking pada Daubechies Transform Berbasis Arnold Cat Map

Digital image security using Transform Domain algorithms such as Discrete Wavelet Transform (DWT) has been widely used. To improve the security of the DWT algorithm needs to randomize the pixel coefficient, namely Arnold Cat Map (ACM). Computing ACM as one of the chaos functions is known to be fast and fits with Transform Domain. DWT has been implemented in the Daubechies filter which is the development of the Haar filer. In this paper, we proposed the message insertion model using a combination of DWT and ACM on a 512x512 piskel grayscale image and a 64x64 pixel message on the LL subband. The experiments were performed on 2 different images to determine the ability produced by the combined algroithm. The ability test for message insertion process is done through Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and comparation between original image histogram and image insertion histogram. While in the process of message extraction, algorithmic capability test is done by calculating Normalized Cross Correlation (NCC) and its correlation. The highest MSE result is 2.9502 and the highest PSNR is 43.4323 dB, while the NCC value is 237.3584 with correlation 0.7181.

Parallel imaging in time-of-flight magnetic resonance angiography using deep multistream convolutional neural networks.

To develop and evaluate a method of parallel imaging time-of-flight (TOF) MRA using deep multistream convolutional neural networks (CNNs). A deep parallel imaging network ("DPI-net") was developed to reconstruct 3D multichannel MRA from undersampled data. It comprises 2 deep-learning networks: a network of multistream CNNs for extracting feature maps of multichannel images and a network of reconstruction CNNs for reconstructing images from the multistream network output feature maps. The images were evaluated using normalized root mean square error (NRMSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) values, and the visibility of blood vessels was assessed by measuring the vessel sharpness of middle and posterior cerebral arteries on axial maximum intensity projection (MIP) images. Vessel sharpness was compared using paired t tests, between DPI-net, 2 conventional parallel imaging methods (SAKE and ESPIRiT), and a deep-learning method (U-net). DPI-net showed superior performance in reconstructing vessel signals in both axial slices and MIP images for all reduction factors. This was supported by the quantitative metrics, with DPI-net showing the lowest NRMSE, the highest PSNR and SSIM (except R = 3.8 on sagittal MIP images, and R = 5.7 on axial slices and sagittal MIP images), and significantly higher vessel sharpness values than the other methods. DPI-net was effective in reconstructing 3D TOF MRA from highly undersampled multichannel MR data, achieving superior performance, both quantitatively and qualitatively, over conventional parallel imaging and other deep-learning methods.

Hardware implementation of adaptive feedback based reversible image watermarking for image processing application

This paper presents a reversible image watermarking (RIW) method including an adaptive feedback part based on difference expansion (DE). With respect to some parameters of the image, peak signal to noise ratio (PSNR), the highest payload capacity and the corresponding embedding threshold are spontaneously calculated by using the proposed adaptive feedback-based reversible Image watermarking (AFRIW). The payload capacity for data embedding is briefly explained. The machinery part of the adaptive feedback for controlling the payload capacity is presented. Software verification of three cover images is presented. Based on some image parameters, the comparative result between the proposed AFRIW algorithm and DE-based RIW method is presented. This paper also presents the VLSI architecture of this proposed algorithm for RIW. The proposed architecture has been implemented using VIVADO 2016.2 based on Xilinx Virtex-7 FPGA and Zynq device platforms. The software implementation results clearly demonstrated that the AFRIW method provides higher PSNR than the DE-based RIW method. The hardware implementation results indicate that the proposed algorithm has low timing complexity over other existing feedback based RIW algorithms which in turn provide higher speed.

Comparison of De-Noising Algorithms Technique

The concept of noise appears during the process of gathering the image into digital form: that is when the image is being created and it may also be introduced when the image is being transmitted. The presence of the noise usually degraded the quality of the image. De noising algorithms were employed in order to advance the value of the image. This paper tries to compare linear and non linear filtering algorithm. This study adopted image processing techniques to process 600 images dataset acquired from 60 different signers using vision based method. The acquired images were de-noised using Gaussian filter and Median filter algorithms. The outcomes of the two de-noising algorithms were compared using Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE). The results of processed images for de-noising algorithms show that Median filter had higher PSNR of 47.7 than the Gaussian filter of 31.79, and lower MSE of 1.11 than Gaussian filter of 43.4.It was also ascertained that de-noised images with non-linear median filter had better quality than images de-noised by linear Gaussian filter.

A general framework for shiftable position-based dual-image reversible data hiding

This paper proposes an improved method for shiftable position-based dual-image reversible data hiding. During the procedure of embedding data, the total number of shiftable coordinates is set as the parameter to make a trade-off between distortion and embedding rate. First, the optimal parameter is sought and the one-to-one code table is generated according to the expected embedding rate. Then, the message data is embedded into the cover image through a simple and effective code table lookup scheme to generate two visually similar stego images. During the procedure of data extraction, the message can be extracted in a reverse manner. The experimental results demonstrate that the proposed method has higher PSNR (peak signal-to-noise ratio) values than our previous work in the case of high embedding rates.

Coded aperture design for solving the phase retrieval problem in X-ray crystallography

X-ray crystallography is an experimental technique used in material analysis that allows to measure the atomic positions of the elements present in a crystal. This technique is based on the X-ray diffraction patterns that provide electronic and elastic properties of the crystal of interest. Thus, the crystal can be uniquely identified by means of the phase of its diffraction patterns that are also used to analyze the material of interest. The phase of the X-ray cannot be directly measured; however, it can be recovered from the intensity of diffraction patterns. A recent work has shown that the phase signal can be recovered more efficiently when the acquisition architecture includes an optical element, known as coded aperture, such that the underlying signal is recovered from coded diffraction patterns. A coded aperture is an element that modulates the X-ray diffraction patterns by blocking some X-ray beams. The structure and the number of coded projections are crucial inasmuch they determine the quality and the acquisition time of the X-ray signal. This paper presents the analysis of a coded X-ray Crystallography system, and the design of the spatial structure of the coded aperture, such that the images are recovered with high PSNR (Peak Signal to Noise Ratio) using the minimum number of coded projections. The simulations indicate that the designed coded apertures obtain a reduction of up to 50% in the number of coded projections and an increase in the PSNR of up to 2 dB when the results are compared with the reconstructed images by using random non-designed coded aperture structures. All simulations were carried out on a set of diffraction pattern images, obtained by using the SAXS/WAXS X-ray crystallography software to simulated the diffraction patterns of a real crystal structure, called Rhombic Dodecahedron.

Image Autoregressive Interpolation Model Using GPU-Parallel Optimization

With the growth in the consumer electronics industry, it is vital to develop an algorithm for ultrahigh definition products that is more effective and has lower time complexity. Image interpolation, which is based on an autoregressive model, has achieved significant improvements compared with the traditional algorithm with respect to image reconstruction, including a better peak signal-to-noise ratio (PSNR) and improved subjective visual quality of the reconstructed image. However, the time-consuming computation involved has become a bottleneck in those autoregressive algorithms. Because of the high time cost, image autoregressive-based interpolation algorithms are rarely used in industry for actual production. In this study, in order to meet the requirements of real-time reconstruction, we use diverse compute unified device architecture (CUDA) optimization strategies to make full use of the graphics processing unit (GPU) (NVIDIA Tesla K80), including a shared memory and register and multi-GPU optimization. To be more suitable for the GPU-parallel optimization, we modify the training window to obtain a more concise matrix operation. Experimental results show that, while maintaining a high PSNR and subjective visual quality and taking into account the I/O transfer time, our algorithm achieves a high speedup of 147.3 times for a Lena image and 174.8 times for a 720p video, compared to the original single-threaded C CPU code with -O2 compiling optimization.

Secured image compression using AES in bandelet domain

Compression and encryption are jointly used in network systems to improve efficiency and security. A secure and reliable means for communicating images and video is, consequently, indispensable for networks. In this paper, a new methodology is proposed for secure image compression. Initially, a bandelet transform is applied to the input image to obtain coefficients and kernel matching pursuits (KMPs) used to choose key bandelet coefficients. The coefficients obtained from the KMP are encrypted using the Advanced Encryption Standard (AES) and encoded using the listless set partitioning embedded block (listless SPECK) image compression encoding technique. For performance evaluation, the peak signal to noise ratio (PSNR), mean square error (MSE), structural similarity index (SSIM) and image quality index (IQI) are taken. From the experimental results and performance evaluation, it is shown that the proposed approach produces high PSNR values and compresses images securely.

Secured image compression using AES in bandelet domain

Compression and encryption are jointly used in network systems to improve efficiency and security. A secure and reliable means for communicating images and video is, consequently, indispensable for networks. In this paper, a new methodology is proposed for secure image compression. Initially, a bandelet transform is applied to the input image to obtain coefficients and kernel matching pursuits (KMPs) used to choose key bandelet coefficients. The coefficients obtained from the KMP are encrypted using the Advanced Encryption Standard (AES) and encoded using the listless set partitioning embedded block (listless SPECK) image compression encoding technique. For performance evaluation, the peak signal to noise ratio (PSNR), mean square error (MSE), structural similarity index (SSIM) and image quality index (IQI) are taken. From the experimental results and performance evaluation, it is shown that the proposed approach produces high PSNR values and compresses images securely.

Chronic Wound Image Analysis by Particle Swarm Optimization Technique for Tele-Wound Network

This paper presents an outline of methods that have been proposed for the analysis of chronic wound images. This paper indicates the details of four different ulcerous cases, provides good treatment policy, enhances quality of patient’s life, improves evidence based clinical outcomes and suggests best possible issues. This paper investigates efficient filtering techniques for chronic wound image pre-processing under Tele-wound network. The aim of this work is to accurately access the healing status of chronic wound with improved image processing techniques by proper filtering. Efficient filtering techniques help to reduce the noise for wound images. The simulation results are presented by comparing different parameters. Performance parameters are peak signal to noise ratio (PSNR), mean square error (MSE), signal to noise ratio and mean absolute error. Results shows adaptive Median filtering provides better performances with respect to high PSNR and reduced MSE between original and filtered image. This work proposes the Particle Swarm Optimization (PSO) method for segmentation of wound areas via suitable color space selection. The PSO algorithm in Db channel provided good accuracy (98.93%) for chronic wound segmentation. Here proposed Linier discriminant analysis classifier provides 98% overall tissue prediction accuracy. The aim is to develop telemedicine framework for wound diagnosis by improving good interaction between health experts, patients, and tele-medical agents who belongs to rural/urban areas that are involved in the provision of care to resolve the delayed treatment.

An adjustable-purpose image watermarking technique by particle swarm optimization

Imperceptibility, security, capacity, and robustness are among many aspects of image watermarking design. An ideal watermarking system should embed a large amount of information perfectly securely, but with no visible degradation to the host image. Many researchers have geared efforts towards developing specific techniques for variant applications. In this paper, we propose an adjustable-purpose, reversible and fragile watermarking scheme for image watermarking by particle swarm optimization (PSO). In general, given any host image and watermark, our scheme can provide an optimal watermarking solution. First, the content of a host image is analyzed to extract significant regions of interest (ROIs) automatically. The remaining regions of non-interest (RONIs) are collated for embedding watermarks by different amounts of bits determined by PSO to achieve optimal watermarking. The parameters can be adjusted relying upon user’s watermarking purposes. Experimental results show that the proposed technique has accomplished higher capacity and higher PSNR (peak signal-to-noise ratio) watermarking.

Research on the Image Denoising Method Based on Partial Differential Equations

AbstractIn this paper we propose a new approach for image denoising based on the combination of PM model, isotropic diffusion model, and TV model. To emphasize the superiority of the proposed model, we have used the Structural Similarity Index Measure (SSIM) and Peak Signal to Noise Ratio (PSNR) as the subjective criterion. Numerical experiments with different images show that our algorithm has the highest PSNR and SS1M, as well as the best visual quality among the six algorithms. Experimental results confirm the high performance of the proposed model compared with some well-known algorithms. In a word, the new model outperforms the mentioned three well known algorithms in reducing the Gibbs-type artifacts, edges blurring, and the block effect, simultaneously.

Video error concealment scheme based on tensor model

Since compressed video sequences may be corrupted or lost when transmitted over error-prone networks, error concealment techniques are very important for video communication. As a video sequence is a group of high-dimensional data, it can be considered as a big 3 r d -order tensor. The methodologies that matricize high-dimensional data and then apply matrix-based method for further analysis often cause a loss of the internal structure information. Therefore, we built a tensor model to process such data, in order to preserve the natural multilinear structure. The key idea of our tensor model includes two parts. The first part is to construct a small tensor consist of the corrupted block and its several reference blocks. This part could be accomplished by block matching, but the traditional block matching might give a wrong match when the corrupted area is large and continuous. In order to overcome such situation, we proposed a flexible block matching scheme (FBM). The second part is to figure out the data in the corrupted part by tensor low rank approximation. Unlike the traditional low rank approximation, we did not fix the rank of core tensor as a constant number. Instead, the rank is flexible in our method, which is adaptive to the situation. Compared with some other error concealment method in the experiments, our method is able to achieve significantly higher PSNR (Peak Signal to Noise Ratio) as well as better visual quality.

An Efficient Image Contrast Enhancement Algorithm Using Genetic Algorithm and Fuzzy Intensification Operator

Image contrast enhancement algorithms play a crucial role in image processing and computer vision. The main challenge in contrast enhancement is that an algorithm suitable for low contrast distorted images does not suit for high contrast distorted images. In this paper, an efficient contrast enhancement algorithm with automated parameterization is proposed using the concept of genetic algorithm and fuzzy intensification operator. Main focus of the proposed method is to improve the visibility information of an image by manipulating their intensity information. Simulation results of the proposed fuzzy-genetic based method were compared with standard existing methods such as log, gamma, linear contrast stretching, histogram equalization, adaptive histogram equalization and rule based fuzzy method using their default parameter values. Performance of the proposed and existing methods on very low, low, moderate, high and very high levels of contrast distorted images were quantitatively measured using peak signal to noise ratio (PSNR), structural similarity index measure (SSIM) and feature similarity index measure (FSIM). The PSNR, SSIM and FSIM values were statistically analysed by two-way ANOVA. Results of this experiment inferred that (a) the contrast enhancement techniques performed well when the level of distortions were very low to moderate, (b) contrast enhancement was better in the proposed fuzzy-genetic based method than other existing methods, and (c) overall, the proposed fuzzy-genetic based method performed well on very low to very high levels of distorted images with higher PSNR, SSIM and FSIM values than other existing methods.