PSNR Measures Research Articles

Enhancing the security of image communication with a new hyper-chaotic system

The rapid expansion of multimedia communication necessitates the development of advanced and resilient cryptographic systems. As computational power grows, traditional encryption methods become susceptible to brute-force attacks. Cybersecurity threats are in a constant state of flux, requiring the creation of innovative cryptographic systems to outpace these developments. Chaotic systems offer a very good promise in cryptography as they are sensitive to initial conditions and parameters. In this study, the authors propose a novel hypercryptographic system (referred as NHCS) for color images that integrates a new hyperchaotic system with the RSA algorithm. This approach combines the unpredictable behavior of the hyper-chaotic system with the robust encryption capabilities of the RSA algorithm. This combination provides an additional layer of security. The hyper-chaotic systems offer a broader range of parameter values leading to chaos. Moreover, due to their four-dimensional nature, they can be employed to encrypt four-color channels. The NHCS algorithm has a large key space (of size 2704), 1024-bit RSA encrypted for higher security. The key sensitivity of this algorithm is of the order 10−16. The effectiveness of NHCS is validated through numerous test images from the USC-SIPI database and is bolstered by statistical analysis. The two indicators NPCR (∽100% ) and UACI (>33%) ensure that the system is robust against differential attacks. We perform a correlation analysis of the images and observe that correlation coefficients between adjacent pixels of the encrypted images are close to zero. Further, the information entropy values of the encrypted images are close to the ideal value 8. PSNR, MSE, and other statistical measures are found near their respective ideal values. A comparison with the other methods shows that NHCS is very promising.

A Framework for Reconstructing Super-Resolution Magnetic Resonance Images from Sparse Raw Data Using Multilevel Generative Methods

Super-resolution magnetic resonance (MR) scans give anatomical data for quantitative analysis and treatment. The use of convolutional neural networks (CNNs) in image processing and deep learning research have led to super-resolution reconstruction methods based on deep learning. The study offers a G-guided generative multilevel network for training 3D neural networks with poorly sampled MR input data. The author suggest using super-resolution reconstruction (SRR) and modified sparse sampling to address these issues. Image-based Wasserstein GANs retain k-space data sparsity. Wasserstein Generative Adversarial Networks (WGANs) store and represent picture space knowledge. The method obtains null-valued k-space data and repairs fill gaps in the dataset to preserve data integrity. The proposed reconstruction method processes raw data samples and is able to perform subspace synchronization, deblurring, denoising, motion estimation, and super-resolution image production. The suggested algorithm uses different preprocessing methods to deblur and denoise datasets. Preliminary trials contextualize and speed up assessments. Results indicate that reconstructed pictures have better high-frequency features than sophisticated multi-frame techniques. This is supported by rising PSNR, MAE, and IEM measurements. A k-space correction block improves GAN network refinement learning in the suggested method. This block improves the network’s ability to avoid unnecessary data, speeding reconstruction. A k-space correction module can limit the generator’s output to critical lines, allowing the reconstruction of only missing lines. This improves convergence and speeds rebuilding. This study shows that this strategy reduces aliasing artifacts better than contemporaneous and noniterative methods.

Applied Improved Canny Edge Detection for Diagnosis Medical Images of Human Brain Tumors

Medical image processing has become one of the crucial elements of the diagnostic process because of the increased usage of medical imaging recently, and clinicians' dependence on such computer-processed medical images in diagnosing patients. As the traditional Canny edge detection algorithm is sensitive to noise, it is easy to lose weak edge information when filtering out the noise, and its fixed parameters show poor adaptability. The suggested algorithm introduced the concept of image block intensity operator to replace image gradient. In addition, the computing speed of the suggested algorithm is relatively fast because it works block by block rather than pixel by pixel. Two adaptive threshold selection methods are presented, one based on the median accumulative histogram of image gradient magnitude and the other on the standard deviation for both types of image pixels (one with less edge information and the other with rich edge information). The proposed algorithm can be dividing into four stages: Input the medical digital image, convert the color medical image to gray-scale, applied improved canny edge detection, then calculate the MSE & PSNR Measures, in addition conduct a visual questionnaire by oncologists to find out which method that made the enhancement of the medical image clearer.

Image contrast enhancement for preserving entropy and image visual features

Histogram equalization is essential for low-contrast enhancement in image processing. Several methods have been proposed; however, one of the most critical problems encountered by existing methods is their ability to preserve information in the enhanced image as the original. This research proposes an image enhancement method based on a histogram equalization approach that preserves the entropy and fine details similar to those of the original image. This is achieved through proposed probability density functions (PDFs) that preserve the small gray values of the usual PDF. The method consists of several steps. First, occurrences and clipped histograms are extracted according to the proposed thresholding. Then, they are equalized and used by a proposed transferring function to calculate the new pixel values in the enhanced image. The proposed method is compared with widely used methods such as Clahe, CS, HE, and GTSHE. Experiments using benchmark datasets and entropy, contrast, PSNR, and SSIM measurements are conducted to evaluate the performance. The results show that the proposed method is the only one that preserves the entropy of the enhanced image of the original image. In addition, it is efficient and reliable in enhancing image quality. This method preserves fine details and improves image quality, supporting computer vision and pattern recognition fields.

A Novel L-CLAHE-Based Intensification Filter for Enhancement of Underwater Images and Pipeline Tracking

The high contrast degraded underwater images often demand extensive pre-processing to attain reliable information for object detection and pipeline tracking. The prototype system developed employs CLAHE, a variant of adaptive histogram equalization, with an adaptive clip limit based on entropy. To yield an enhanced image, CLAHE is applied to the L* channel alone, resulting in improved L- CLAHE and better results. Validation of the improved L-CLAHE method is carried out using real-life underwater images captured using DTG2 Pro ROV from Kochi backwaters based on PSNR, SSIM, EPI, and PFOM quality measures and outperforms the conventional histogram equalization and adaptive histogram equalization techniques. These images are subjected to intensification and filtering for further enhancement as well as object detection and pipeline tracking applications. The proposed algorithm is compared with CLAHE, MSRCR, and L-CIF methods. The improved L-CIF algorithm has a high entropy value compared to the existing algorithms while the EME metric has been evaluated close to unity indicating the improved performance of the fast-guided filter. The method developed also helps in improving the average pixel intensity of the input images.

Denoising digital breast tomosynthesis projections using deep learning with synthetic data as training set.

Image denoising based on deep neural networks (DNN) needs a big dataset containing digital breast tomosynthesis (DBT) projections acquired in different radiation doses to be trained, which is impracticable. Therefore, we propose extensively investigating the use of synthetic data generated by software for training DNNs to denoise DBT real data. The approach consists of generating a synthetic dataset representative of the DBT sample space by software, containing noisy and original images. Synthetic data were generated in two different ways: (a)virtual DBT projections generated by OpenVCT and (b)noisy images synthesized from photography regarding noise models used in DBT (e.g., Poisson-Gaussian noise). Then, DNN-based denoising techniques were trained using a synthetic dataset and tested for denoising physical DBT data. Results were evaluated in quantitative (PSNR and SSIM measures) and qualitative (visual analysis) terms. Furthermore, a dimensionality reduction technique (t-SNE) was used for visualization of sample spaces of synthetic and real datasets. The experiments showed that training DNN models with synthetic data could denoise DBT real data, achieving competitive results to traditional methods in quantitative terms but showing a better balance between noise filtering and detail preservation in a visual analysis. T-SNE enables us to visualize if synthetic and real noises are in the same sample space. We propose a solution for the lack of suitable training data to train DNN models for denoising DBT projections, showing that we just need the synthesized noise to be in the same sample space as the target image.

Multimodality MRI synchronous construction based deep learning framework for MRI-guided radiotherapy synthetic CT generation

Synthesizing computed tomography (CT) images from magnetic resonance imaging (MRI) data can provide the necessary electron density information for accurate dose calculation in the treatment planning of MRI-guided radiation therapy (MRIgRT). Inputting multimodality MRI data can provide sufficient information for accurate CT synthesis: however, obtaining the necessary number of MRI modalities is clinically expensive and time-consuming. In this study, we propose a multimodality MRI synchronous construction based deep learning framework from a single T1-weight (T1) image for MRIgRT synthetic CT (sCT) image generation. The network is mainly based on a generative adversarial network with sequential subtasks of intermediately generating synthetic MRIs and jointly generating the sCT image from the single T1 MRI. It contains a multitask generator and a multibranch discriminator, where the generator consists of a shared encoder and a splitted multibranch decoder. Specific attention modules are designed within the generator for feasible high-dimensional feature representation and fusion. Fifty patients with nasopharyngeal carcinoma who had undergone radiotherapy and had CT and sufficient MRI modalities scanned (5550 image slices for each modality) were used in the experiment. Results showed that our proposed network outperforms state-of-the-art sCT generation methods well with the least MAE, NRMSE, and comparable PSNR and SSIM index measure. Our proposed network exhibits comparable or even superior performance than the multimodality MRI-based generation method although it only takes a single T1 MRI image as input, thereby providing a more effective and economic solution for the laborious and high-cost generation of sCT images in clinical applications.

A Novel Image Similarity Measure Based on Greatest and Smallest Eigen Fuzzy Sets

A novel image similarity index based on the greatest and smallest fuzzy set solutions of the max–min and min–max compositions of fuzzy relations, respectively, is proposed. The greatest and smallest fuzzy sets are found symmetrically as the min–max and max–min solutions, respectively, to a fuzzy relation equation. The original image is partitioned into squared blocks and the pixels in each block are normalized to [0, 1] in order to have a fuzzy relation. The greatest and smallest fuzzy sets, found for each block, are used to measure the similarity between the original image and the image reconstructed by joining the squared blocks. Comparison tests with other well-known image metrics are then carried out where source images are noised by applying Gaussian filters. The results show that the proposed image similarity measure is more effective and robust to noise than the PSNR and SSIM-based measures.

Mitigation of motion-induced artifacts in cone beam computed tomography using deep convolutional neural networks.

Cone beam computed tomography (CBCT) is often employed on radiation therapy treatment devices (linear accelerators) used in image-guided radiation therapy (IGRT). For each treatment session, it is necessary to obtain the image of the day in order to accurately position the patient and to enable adaptive treatment capabilities including auto-segmentation and dose calculation. Reconstructed CBCT images often suffer from artifacts, in particular those induced by patient motion. Deep-learning based approaches promise ways to mitigate such artifacts. We propose a novel deep-learning based approach with the goal to reduce motion induced artifacts in CBCT images and improve image quality. It is based on supervised learning and includes neural network architectures employed as pre- and/or post-processing steps during CBCT reconstruction. Our approach is based on deep convolutional neural networks which complement the standard CBCT reconstruction, which is performed either with the analytical Feldkamp-Davis-Kress (FDK) method, or with an iterative algebraic reconstruction technique (SART-TV). The neural networks, which are based on refined U-net architectures, are trained end-to-end in a supervised learning setup. Labeled training data are obtained by means of a motion simulation, which uses the two extreme phases of 4D CT scans, their deformation vector fields, as well as time-dependent amplitude signals as input. The trained networks are validated against ground truth using quantitative metrics, as well as by using real patient CBCT scans for a qualitative evaluation by clinicalexperts. The presented novel approach is able to generalize to unseen data and yields significant reductions in motion induced artifacts as well as improvements in image quality compared with existing state-of-the-art CBCT reconstruction algorithms (up to +6.3 dB and +0.19 improvements in peak signal-to-noise ratio, PSNR, and structural similarity index measure, SSIM, respectively), as evidenced by validation with an unseen test dataset, and confirmed by a clinical evaluation on real patient scans (up to 74% preference for motion artifact reduction over standard reconstruction). For the first time, it is demonstrated, also by means of clinical evaluation, that inserting deep neural networks as pre- and post-processing plugins in the existing 3D CBCT reconstruction and trained end-to-end yield significant improvements in image quality and reduction of motionartifacts.

Fourier Transform with Noisy Image

The noise is a major problem in computer vision because it affects image quality and impede the illation. In this paper, we suggest method works on instantaneous transformations that are used in the analysis of the signals representing the image and the frequencies they contain, through which the image can be retrieved or the areas containing the noise where the image is transformed within the complex range and then the original image. Result of this paper are 0.9804 in case SSIM measure and 32.9850 in case PSNR measure.

Feature Map Regularized CycleGAN for Domain Transfer

CycleGAN domain transfer architectures use cycle consistency loss mechanisms to enforce the bijectivity of highly underconstrained domain transfer mapping. In this paper, in order to further constrain the mapping problem and reinforce the cycle consistency between two domains, we also introduce a novel regularization method based on the alignment of feature maps probability distributions. This type of optimization constraint, expressed via an additional loss function, allows for further reducing the size of the regions that are mapped from the source domain into the same image in the target domain, which leads to mapping closer to the bijective and thus better performance. By selecting feature maps of the network layers with the same depth d in the encoder of the direct generative adversarial networks (GANs), and the decoder of the inverse GAN, it is possible to describe their d-dimensional probability distributions and, through novel regularization term, enforce similarity between representations of the same image in both domains during the mapping cycle. We introduce several ground distances between Gaussian distributions of the corresponding feature maps used in the regularization. In the experiments conducted on several real datasets, we achieved better performance in the unsupervised image transfer task in comparison to the baseline CycleGAN, and obtained results that were much closer to the fully supervised pix2pix method for all used datasets. The PSNR measure of the proposed method was, on average, 4.7% closer to the results of the pix2pix method in comparison to the baseline CycleGAN over all datasets. This also held for SSIM, where the described percentage was 8.3% on average over all datasets.

Safeguarding Digital Essence: A Sub-band DCT Neural Watermarking Paradigm Leveraging GRNN and CNN for Unyielding Image Protection and Identification

Image watermarking preserves digital content. This study introduces a new watermarking approach employing Sub-Band Discrete Cosine Transform and Deep neural networks, GRNN and CNN. The method embeds robust, invisible watermarks in greyscale photos and compares the two neural network topologies. The watermark is added using sub-band DCT. Watermark embedding in low-frequency sub-bands resists photo processing. The binary watermark modifies sub-band DCT coefficients to determine embedding intensity, resisting signal deterioration, and assaults. GRNN and CNN neural networks extract watermarks accurately. CNN extracts hierarchical features from images, enabling robust watermark recovery even under distortions, whereas non-parametric GRNN stores the whole training data to create predictions. The watermarking approach is tested on several greyscale photos. PSNR, SSIM, MSE, and NCC measure performance. The watermark tests noise addition, compression, and filtering. Compare GRNN and CNN's watermark extraction strengths and shortcomings to assess image watermarking suitability.

Uzamsal Alanda Bir Kripto-Stegano Hibrit Uygulama

The need for data security in communication is increasing with the developing
 technology. The best way to ensure data security is cryptography. Humanity has
 proposed many cryptography methods for hundreds of years. Today, there are
 complex cryptography algorithms, which are quite secure but require high processing
 power. Cryptography techniques used in the past are less secure but require less
 processing power. This article proposes an extended version of the Vigenère cipher,
 which is an ancient encryption method. Extended Vigenère cipher is able to encrypt
 upper and lower case letters, numeric characters, and punctuation marks. In addition,
 the method can use upper and lower case letters, numeric characters and punctuation
 marks as encryption key, and there is no key length limit. The cipher text generated
 with the extended Vigenère cipher is also hidden in an image to keep it safe in the
 transmission medium. Hiding is performed with LSB steganography, which is
 another data security method. Thus, both the readability and detectability of
 confidential data are reduced. The cryptological security of the proposed method is
 measured by the Avalanche effect. Steganographic safety is evaluated with SSIM
 and PSNR measurements. A user graphical interface has been developed and the
 maximum payload of the carrier image is measured before encryption. Proposed
 method applied on some test images with different specifications. 1 bit key
 modification avalanche effect results of proposed method with random plain texts
 between 1000-10000 characters with a key length of 8 and 16 characters is close to
 50%. Also, SSIM value calculated over 0.99 each time while maximum
 steganographic payload of the test images reached.

Wavelet -Based Modified Intermediate Significant Bit Insertion for Text Steganography

In this paper, we propose and investigate the use of a modifiedversion of Intermediate Significant Bit (ISB) insertion algorithm, so-called here as MISB algorithm for secret text steganography. The secret text is passed through a sequence of ciphering techniques. A Modified Intermediate Significant Bit MISB insertion algorithm is proposed for hiding the bit sequences of the cipher text into the time-frequency domain using wavelet transform of the image pixels, in order to improve the robustness of the Steganography system. Results demonstrate the effectiveness of wavelet Modified Intermediate Significant Bit WMISB while evaluating its performance using Peak Signal to Noise Ratio PSNR measure. Moreover, additional experiments are evaluated for noising, and image compression for cover images to compare their Peak Signal to Noise Ratio PSNR. The drawn results confirm the effectiveness of the proposed Wavelet-based Modified Intermediate Significant Bit Insertion WMISB, Also, Bit Error Rate BER, is a key parameter that is used in assessing systems that transmit digital data from one location to another.

Superresolution Reconstruction in Automatic Thai Sign Language Feature Extraction Using Adaptive Triangulation Interpolation

Superresolution is an image processing technique for improving image quality and enhancing low-resolution images. This paper presents a novel interpolation method for increasing superresolution reconstruction effectiveness using a triangulation interpolation algorithm for automatic Thai sign language feature extraction. This approach uses three neighboring pixels for estimation. The experiment compared the superresolution reconstruction performance using the triangulation interpolation algorithm and the nearest-neighbor, SRCNN, bilinear, bicubic, GPR, and NEDI methods. The super-resolution reconstruction using the triangulation-improved interpolation technique provided the best PSNR measurements of image quality between the original and superresolution-reconstructed images. The PSNR value of the sign language image was 40.608, improving performance by 13.15%. The Thai sign language gesture recognition using 2D convolutional neural networks showed that the designed model increased the gesture recognition effectiveness with an accuracy of 0.95 and loss of 0.14. Thus, this study provides state-of-the-art superresolution reconstruction for automatic Thai sign language gesture recognition.

Compression of image using multi-wavelet techniques

Digital compression of images is a topic that has appeared in a lot of studies over the past decade to this day. As wavelet transform algorithms advance and procedures of quantization have helped to bypass current compression of image standards such as the JPEG algorithm. To get the highest effectiveness in compression of image transforms of wavelet need filters which gather a desirable character's number i.e., symmetry and orthogonally. Nevertheless, wave design capabilities are restricted due to their ability to have all of such desirable characters at the same time. The multi-wavelet technology removes a few of the restrictions of the wavelet play more than the options of design and thus able to gather all desired Characters of transforming. Wavelet andmulti-wave filter banks are tested on a larger scale with images, providing more useful analysis. Multiple waves indicate energy-compression efficiency (a higher compression ratio usually indicates a higher mean square error, MSE, in the compressed image). Filter bank Characters such as orthogonal and compact support, symmetry, and phase response are important factors that also affect MSE and professed quality of the image. The current work analyzes the multi-wave Characters effect on the performance of compression of images. Four multi-wavelength Characters (GHM, CL, ORT4) were used in this thesis and the compression of image performance of grayscale images was compared with common scalar waves (D4). SPIHT quantification device in stress chart and use of PSNR and subjective quality measures to assess performance. The results in this paper point out those multi wave characteristics that are most important for the compression of images. Moreover, PSNR results and subjective quality show similar performance to the best scalar and multi-waves. The analysis also shows that a programmer based on multi-band conversion significantly improves the perceived image quality.

Non-Local Robust Quaternion Matrix Completion for Large-Scale Color Image and Video Inpainting.

The image nonlocal self-similarity (NSS) prior refers to the fact that a local patch often has many nonlocal similar patches to it across the image and has been widely applied in many recently proposed machining learning algorithms for image processing. However, there is no theoretical analysis on its working principle in the literature. In this paper, we discover a potential causality between NSS and low-rank property of color images, which is also available to grey images. A new patch group based NSS prior scheme is proposed to learn explicit NSS models of natural color images. The numerical low-rank property of patched matrices is also rigorously proved. The NSS-based QMC algorithm computes an optimal low-rank approximation to the high-rank color image, resulting in high PSNR and SSIM measures and particularly the better visual quality. A new tensor NSS-based QMC method is also presented to solve the color video inpainting problem based on quaternion tensor representation. The numerical experiments on color images and videos indicate the advantages of NSS-based QMC over the state-of-the-art methods.

Image Matting using Neural Networks

Image matting, also refers to picture matting in the article, is the task of finding appealing targets in a picture or sequence of pictures i.e., video, and it has been used extensively in many photo and video editing applications. Image composition is the process of extracting an eye-catching subject from a photograph and blending it with a different background. a) Blue/Green screen (curtain) matting, where the backdrop is clear and readily distinct between the foreground (frontal area) and background (foundation) portions. This approach is now the most used type of image matting. b) Natural picture matting, in which these sorts of photos are taken naturally using cameras or cell phones during everyday activities. These are the present known techniques of picture matting. It is difficult to discern the distinction between the frontal area and the foundation at their boundaries. The current framework requires both the RGB and trimap images as inputs for natural picture matting. It is difficult to compute the trimap since additional framework is required to obtain this trimap. This study will introduce the Picture Matting Neural Net (PMNN) framework, which utilizes a single RGB image as an input and creates the alpha matte without any human involvement in between the framework and the user, to overcome the drawbacks of the prior frameworks. The created alpha matte is tested against the alpha matte from the PPM-100 data set, and the PSNR and SSIM measurement index are utilized to compare the two. The framework works well and can be fed with regular pictures taken with cameras or mobile phones without reducing the clarity of the image.

An effective image compression technique based on burrows wheeler transform with set partitioning in hierarchical trees

AbstractIn this article, the efficient image compression which consists of Burrows–Wheeler transform (BWT) with set partitioning in hierarchical trees (SPIHT). The main phases of the proposed system are: partitioning, compression of non‐ROI areas, Fusion and compression of ROI areas. To enhance the propose of the proposed methodology, the morphological functions are updated by dividing two types of images with the consideration of dilation and erosion control. After that, the convolution and correlation in the deformation provide good accuracy of the segmentation at the fastest speed. In this proposed methodology, SPIHT encryption is a lossy compression technique aimed at understanding the non‐ROI area, while the BWT is a lossless compression technique performed to achieve a summary of the ROI area. Finally, separating these two parts of the image merges the image and reconstructs it to the desired quality. The test sends a variety of images and analyzes the performance of the proposed system using compression ratio and PSNR measurements.

Simultaneous image reconstruction and lesion segmentation in accelerated MRI using multitasking learning.

Magnetic resonance imaging (MRI) serves as an important medical imaging modality for a variety of clinical applications. However, the problem of long imaging time limited its wide usage. In addition, prolonged scan time will cause discomfort to the patient, leading to severe image artifacts. On the other hand, manually lesion segmentation is time consuming. Algorithm-based automatic lesion segmentation is still challenging, especially for accelerated imaging with low quality. In this paper, we proposed a multitask learning-based method to perform image reconstruction and lesion segmentation simultaneously, called "RecSeg". Our hypothesis is that both tasks can benefit from the usage of the proposed combined model. In the experiment, we validated the proposed multitask model on MR k-space data with different acceleration factors (2×, 4×, and 6×). Two connected U-nets were used for the tasks of liver and renal image reconstruction and segmentation. A total of 50 healthy subjects and 100 patients with hepatocellular carcinoma were included for training and testing. For the segmentation part, we use healthy subjects to verify organ segmentation, and hepatocellular carcinoma patients to verify lesion segmentation. The organs and lesions were manually contoured by an experienced radiologist. Experimental results show that the proposed RecSeg yielded the highest PSNR (RecSeg: 32.39 ± 1.64 vs. KSVD: 29.53 ± 2.74 and single U-net: 31.18 ± 1.68, respectively, p<0.05) and highest structural similarity index measure (SSIM) (RecSeg: 0.93 ± 0.01 vs. KSVD: 0.88 ± 0.02 and single U-net: 0.90 ± 0.01, respectively, p<0.05) under 6× acceleration. Moreover, in the task of lesion segmentation, it is proposed that RecSeg produced the highest Dice score (RecSeg: 0.86 ± 0.01 vs. KSVD: 0.82 ± 0.01 and single U-net: 0.84 ± 0.01, respectively, p<0.05). This study focused on the simultaneous reconstruction of medical images and the segmentation of organs and lesions. It is observed that the multitask learning-based method can improve performances of both image reconstruction and lesion segmentation.