Mean Peak Signal-to-noise Ratio Research Articles

Synthesis of T2-weighted images from proton density images using a generative adversarial network in a temporomandibular joint magnetic resonance imaging protocol.

This study proposed a generative adversarial network (GAN) model for T2-weighted image (WI) synthesis from proton density (PD)-WI in a temporomandibular joint (TMJ) magnetic resonance imaging (MRI) protocol. From January to November 2019, MRI scans for TMJ were reviewed and 308 imaging sets were collected. For training, 277 pairs of PD- and T2-WI sagittal TMJ images were used. Transfer learning of the pix2pix GAN model was utilized to generate T2-WI from PD-WI. Model performance was evaluated with the structural similarity index map (SSIM) and peak signal-to-noise ratio (PSNR) indices for 31 predicted T2-WI (pT2). The disc position was clinically diagnosed as anterior disc displacement with or without reduction, and joint effusion as present or absent. The true T2-WI-based diagnosis was regarded as the gold standard, to which pT2-based diagnoses were compared using Cohen's ĸ coefficient. The mean SSIM and PSNR values were 0.4781(±0.0522) and 21.30(±1.51) dB, respectively. The pT2 protocol showed almost perfect agreement (ĸ=0.81) with the gold standard for disc position. The number of discordant cases was higher for normal disc position (17%) than for anterior displacement with reduction (2%) or without reduction (10%). The effusion diagnosis also showed almost perfect agreement (ĸ=0.88), with higher concordance for the presence (85%) than for the absence (77%) of effusion. The application of pT2 images for a TMJ MRI protocol useful for diagnosis, although the image quality of pT2 was not fully satisfactory. Further research is expected to enhance pT2 quality.

ALWT based Regularizer for Improvement of Low Intensity Visual Data

Image Completion is the process of recovering corrupted image with very limited observations. It is a challenging task to achieve accurate recovery in image with minimum observations. Many researchers are proposed various methods to recover the corrupted image. Here a new Adaptive Lifting Wavelet Transform (ALWT) based Alternating Direction Method of Multipliers (ADMM) optimization technique is proposed. A spiffing image recovery is observed at 90% of missing ratio. The non-linear filters are used in ALWT to obtain the lost observations. The Image Quality Assessment (IQA) metrics are considered to evaluate the performance of proposed approach. The IQA metrics namely Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) are achieved 32.32, 33.04dB and 0.8666 respectively. Here, the Additive White Gaussian Noise (AWGN) with standard deviation 150 is considered. The Mean Absolute Reconstruction Error (MARE), MSE and PSNR values are obtained as 3.176, 12.11 and 37.31dB respectively.

Analisis Perbandingan Kompresi Suara Menggunakan Principal Component Analysis dan Transformasi Wavelet

One of the requirements faced as a result of information technology development is memory and transmission efficiency. This requirement can be overcome with data compression. Compression is a method to obtain compact data with a smaller size but still maintaining similarity to the original data. Principal Component Analysis (PCA) is an algorithm in machine learning that is used to reduce dimensions. Dimensional reduction is a process of transforming high-dimensional data into new subspaces with lower dimensions. The goal is to use some principal components to represents the original data. Wavelet transformation represents a signal into a set of basic functions through filter analysis. Wavelets concentrate information into coefficients of approximation and coefficients of detail. Wavelet transform produces a lot of zero or close to zero coefficients that can be neglected so it can reduce storage space. In this research, we will propose the implementation of PCA and Wavelet for digital audio compression. The audio was performed with the .wav format. The compressed audio will be evaluated based on Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE). The mean PSNR obtained when using a wavelet is 47.61601 dB with an average MSE of 3.76 x 10-5. Meanwhile, when using PCA, the PSNR average was 57.3962772 dB and the average MSE obtained was 4.59 x 10-5. Four out of five compressed audio had a larger PSNR and smaller MSE when using PCA. Thus, the Principal Component Analysis algorithm can be better used for audio compression than the level 1 of Symlet Wavelet Transformation.

Cone Beam CT (CBCT) Based Synthetic CT Generation Using Deep Learning Methods for Dose Calculation of Nasopharyngeal Carcinoma Radiotherapy.

Objective: To generate synthetic CT (sCT) images with high quality from CBCT and planning CT (pCT) for dose calculation by using deep learning methods. Methods: 169 NPC patients with a total of 20926 slices of CBCT and pCT images were included. In this study the CycleGAN, Pix2pix and U-Net models were used to generate the sCT images. The Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Peak Signal to Noise Ratio (PSNR), and Structural Similarity Index (SSIM) were used to quantify the accuracy of the proposed models in a testing cohort of 34 patients. Radiation dose were calculated on pCT and sCT following the same protocol. Dose distributions were evaluated for 4 patients by comparing the dose-volume-histogram (DVH) and 2D gamma index analysis. Results: The average MAE and RMSE values between sCT by three models and pCT reduced by 15.4 HU and 26.8 HU at least, while the mean PSNR and SSIM metrics between sCT by different models and pCT added by 10.6 and 0.05 at most, respectively. There were only slight differences for DVH of selected contours between different plans. The passing rates of 2D gamma index analysis under 3 mm/3% 3 mm/2%, 2 mm/3%and 2 mm/2% criteria were all higher than 95%. Conclusions: All the sCT had achieved better evaluation metrics than those of original CBCT, while the performance of CycleGAN model was proved to be best among three methods. The dosimetric agreement confirmed the HU accuracy and consistent anatomical structures of sCT by deep learning methods.

Symmetric Cryptography With a Chaotic Map and a Multilayer Machine Learning Network for Physiological Signal Infosecurity: Case Study in Electrocardiogram

Digital physiological signals in telecare medicine information systems have been widely applied in remote medical applications, such as telecare, tele-examination, and telediagnosis, via computer networking transmission or wireless communication. However, these medical records need to ensure authorization demands in the channel model for human body communication and remote medical servers and enhance the confidentiality, recoverability, and availability of transmission data. Hence, this study proposes a symmetric cryptography scheme with a chaotic map and a multilayer machine learning network (MMLN) to achieve physiological signal infosecurity. A chaotic pseudorandom number generator within specific control parameters can dynamically produce unordered sequence numbers to set the secret keys for a regular secret key update, thereby improving the security of private cipher codes. The chaotic map is quickly iterated to produce a pseudorandom key stream for real-time applications, and the private cipher codes are selected using the initial and specific control parameters at the data emitter and receiver ends. A general regression neural network is used to map the high-dimensional input–output pair of cipher codes for substitution and permutation processes. Its adaptive MMLN with an optimization algorithm can rapidly train the random cipher code protocol to achieve an encryptor and a decryptor for a regular encrypted communication. Using the Massachusetts Institute of Technology–Beth Israel Hospital (MIT–BIH) Arrhythmia Database, 100 electrocardiogram fragments are used to verify the proposed model, and the peak signal-to-noise ratio (PSNR) as a quantitative quality metric is used to evaluate the visual quality after encryption and decryption processes for further diagnosis applications. Experimental results show that the proposed scheme has a higher mean PSNR (35.26 ± 3.77 dB) and shorter mean executing time (0.16 ± 0.01 s) compared with traditional cryptography protocol schemes.

Removal of manually induced artifacts in ultrasound images of thyroid nodules based on edge-connection and Criminisi image restoration algorithm

Background and ObjectiveThere are various artificial markers in ultrasound images of thyroid nodules, which have impact on subsequent processing and computer-aided diagnosis. The purpose of this study was to develop an approach to automatically remove artifacts and restore ultrasound images of thyroid nodules. MethodsFifty ultrasound images with manually induced artifacts were selected from publicly available and self-collected datasets. A combined approach was developed which consisted of two steps, artifacts detection and removal of the detected artifacts. Specifically, a novel edge-connection algorithm was used for artifact detection, detection accuracy and false discovery rate were used to evaluate the performance of artifact detection approaches. Criminisi algorithm was used for image restoration with peak signal-to-noise ratio (PSNR) and mean gradient difference to evaluate its performance. In addition, computation complexity was evaluated by execution time of relevant algorithms. ResultsResults revealed that the proposed joint approach with edge-connection and Criminisi algorithm could achieve automatic artifacts removal. Mean detection accuracy and mean false discovery rate of the proposed edge-connection algorithm for the 50 ultrasound images were 0.86 and 1.50. Mean PSNR of the 50 restored images by Criminisi algorithm was 36.64 dB, and mean gradient difference of the restored images was -0.002 compared with the original images. ConclusionsThe proposed combined approach had a good detection accuracy for different types of manually induced artifacts, and could significantly improve PSNR of the ultrasound images. The proposed combined approach may have potential use for the repair of ultrasound images with artifacts.

Simulation Study of Low-Dose Sparse-Sampling CT with Deep Learning-Based Reconstruction: Usefulness for Evaluation of Ovarian Cancer Metastasis

The usefulness of sparse-sampling CT with deep learning-based reconstruction for detection of metastasis of malignant ovarian tumors was evaluated. We obtained contrast-enhanced CT images (n = 141) of ovarian cancers from a public database, whose images were randomly divided into 71 training, 20 validation, and 50 test cases. Sparse-sampling CT images were calculated slice-by-slice by software simulation. Two deep-learning models for deep learning-based reconstruction were evaluated: Residual Encoder-Decoder Convolutional Neural Network (RED-CNN) and deeper U-net. For 50 test cases, we evaluated the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) as quantitative measures. Two radiologists independently performed a qualitative evaluation for the following points: entire CT image quality; visibility of the iliac artery; and visibility of peritoneal dissemination, liver metastasis, and lymph node metastasis. Wilcoxon signed-rank test and McNemar test were used to compare image quality and metastasis detectability between the two models, respectively. The mean PSNR and SSIM performed better with deeper U-net over RED-CNN. For all items of the visual evaluation, deeper U-net scored significantly better than RED-CNN. The metastasis detectability with deeper U-net was more than 95%. Sparse-sampling CT with deep learning-based reconstruction proved useful in detecting metastasis of malignant ovarian tumors and might contribute to reducing overall CT-radiation exposure.

Deep Learning Approach for Generating MRA Images From 3D Quantitative Synthetic MRI Without Additional Scans.

Quantitative synthetic magnetic resonance imaging (MRI) enables synthesis of various contrast-weighted images as well as simultaneous quantification of T1 and T2 relaxation times and proton density. However, to date, it has been challenging to generate magnetic resonance angiography (MRA) images with synthetic MRI. The purpose of this study was to develop a deep learning algorithm to generate MRA images based on 3D synthetic MRI raw data. Eleven healthy volunteers and 4 patients with intracranial aneurysms were included in this study. All participants underwent a time-of-flight (TOF) MRA sequence and a 3D-QALAS synthetic MRI sequence. The 3D-QALAS sequence acquires 5 raw images, which were used as the input for a deep learning network. The input was converted to its corresponding MRA images by a combination of a single-convolution and a U-net model with a 5-fold cross-validation, which were then compared with a simple linear combination model. Image quality was evaluated by calculating the peak signal-to-noise ratio (PSNR), structural similarity index measurements (SSIMs), and high frequency error norm (HFEN). These calculations were performed for deep learning MRA (DL-MRA) and linear combination MRA (linear-MR), relative to TOF-MRA, and compared with each other using a nonparametric Wilcoxon signed-rank test. Overall image quality and branch visualization, each scored on a 5-point Likert scale, were blindly and independently rated by 2 board-certified radiologists. Deep learning MRA was successfully obtained in all subjects. The mean PSNR, SSIM, and HFEN of the DL-MRA were significantly higher, higher, and lower, respectively, than those of the linear-MRA (PSNR, 35.3 ± 0.5 vs 34.0 ± 0.5, P < 0.001; SSIM, 0.93 ± 0.02 vs 0.82 ± 0.02, P < 0.001; HFEN, 0.61 ± 0.08 vs 0.86 ± 0.05, P < 0.001). The overall image quality of the DL-MRA was comparable to that of TOF-MRA (4.2 ± 0.7 vs 4.4 ± 0.7, P = 0.99), and both types of images were superior to that of linear-MRA (1.5 ± 0.6, for both P < 0.001). No significant differences were identified between DL-MRA and TOF-MRA in the branch visibility of intracranial arteries, except for ophthalmic artery (1.2 ± 0.5 vs 2.3 ± 1.2, P < 0.001). Magnetic resonance angiography generated by deep learning from 3D synthetic MRI data visualized major intracranial arteries as effectively as TOF-MRA, with inherently aligned quantitative maps and multiple contrast-weighted images. Our proposed algorithm may be useful as a screening tool for intracranial aneurysms without requiring additional scanning time.

MRI-only based synthetic CT generation using dense cycle consistent generative adversarial networks.

Automated synthetic computed tomography (sCT) generation based on magnetic resonance imaging (MRI) images would allow for MRI-only based treatment planning in radiation therapy, eliminating the need for CT simulation and simplifying the patient treatment workflow. In this work, the authors propose a novel method for generation of sCT based on dense cycle-consistent generative adversarial networks (cycle GAN), a deep-learning based model that trains two transformation mappings (MRI to CT and CT to MRI) simultaneously. The cycle GAN-based model was developed to generate sCT images in a patch-based framework. Cycle GAN was applied to this problem because it includes an inverse transformation from CT to MRI, which helps constrain the model to learn a one-to-one mapping. Dense block-based networks were used to construct generator of cycle GAN. The network weights and variables were optimized via a gradient difference (GD) loss and a novel distance loss metric between sCT and original CT. Leave-one-out cross-validation was performed to validate the proposed model. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and normalized cross correlation (NCC) indexes were used to quantify the differences between the sCT and original planning CT images. For the proposed method, the mean MAE between sCT and CT were 55.7 Hounsfield units (HU) for 24 brain cancer patients and 50.8HU for 20 prostate cancer patients. The mean PSNR and NCC were 26.6dB and 0.963 in the brain cases, and 24.5dB and 0.929 in the pelvis. We developed and validated a novel learning-based approach to generate CT images from routine MRIs based on dense cycle GAN model to effectively capture the relationship between the CT and MRIs. The proposed method can generate robust, high-quality sCT in minutes. The proposed method offers strong potential for supporting near real-time MRI-only treatment planning in the brain and pelvis.

Learning-based CBCT correction using alternating random forest based on auto-context model.

Quantitative Cone Beam CT (CBCT) imaging is increasing in demand for precise image-guided radiotherapy because it provides a foundation for advanced image-guided techniques, including accurate treatment setup, online tumor delineation, and patient dose calculation. However, CBCT is currently limited only to patient setup in the clinic because of the severe issues in its image quality. In this study, we develop a learning-based approach to improve CBCT's image quality for extended clinical applications. An auto-context model is integrated into a machine learning framework to iteratively generate corrected CBCT (CCBCT) with high-image quality. The first step is data preprocessing for the built training dataset, in which uninformative image regions are removed, noise is reduced, and CT and CBCT images are aligned. After a CBCT image is divided into a set of patches, the most informative and salient anatomical features are extracted to train random forests. Within each patch, alternating RF is applied to create a CCBCT patch as the output. Moreover, an iterative refinement strategy is exercised to enhance the image quality of CCBCT. Then, all the CCBCT patches are integrated to reconstruct final CCBCT images. The learning-based CBCT correction algorithm was evaluated using the leave-one-out cross-validation method applied on a cohort of 12 patients' brain data and 14 patients' pelvis data. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR), normalized cross-correlation (NCC) indexes, and spatial nonuniformity (SNU) in the selected regions of interest (ROIs) were used to quantify the proposed algorithm's correction accuracy and generat the following results: mean MAE=12.81±2.04 and 19.94±5.44HU, mean PSNR=40.22±3.70 and 31.31±2.85dB, mean NCC=0.98±0.02 and 0.95±0.01, and SNU=2.07±3.36% and 2.07±3.36% for brain and pelvis data. Preliminary results demonstrated that the novel learning-based correction method can significantly improve CBCT image quality. Hence, the proposed algorithm is of great potential in improving CBCT's image quality to support its clinical utility in CBCT-guided adaptive radiotherapy.

Hyperspectral Mixed Denoising via Spectral Difference-Induced Total Variation and Low-Rank Approximation

Exploration of multiple priors on observed signals has been demonstrated to be one of the effective ways for recovering underlying signals. In this paper, a new spectral difference-induced total variation and low-rank approximation (termed SDTVLA) method is proposed for hyperspectral mixed denoising. Spectral difference transform, which projects data into spectral difference space (SDS), has been proven to be powerful at changing the structures of noises (especially for sparse noise with a specific pattern, e.g., stripes or dead lines present at the same position in a series of bands) in an original hyperspectral image (HSI), thus allowing low-rank techniques to get rid of mixed noises more efficiently without treating them as low-rank features. In addition, because the neighboring pixels are highly correlated and the spectra of homogeneous objects in a hyperspectral scene are always in the same low-dimensional manifold, we are inspired to combine total variation and the nuclear norm to simultaneously exploit the local piecewise smoothness and global low rankness in SDS for mixed noise reduction of HSI. Finally, the alternating direction methods of multipliers (ADMM) is employed to effectively solve the SDTVLA model. Extensive experiments on three simulated and two real HSI datasets demonstrate that, in terms of quantitative metrics (i.e., the mean peak signal-to-noise ratio (MPSNR), the mean structural similarity index (MSSIM) and the mean spectral angle (MSA)), the proposed SDTVLA method is, on average, 1.5 dB higher MPSNR values than the competitive methods as well as performing better in terms of visual effect.

Hyperspectral Image Restoration under Complex Multi-Band Noises

Hyperspectral images (HSIs) are always corrupted by complicated forms of noise during the acquisition process, such as Gaussian noise, impulse noise, stripes, deadlines and so on. Specifically, different bands of the practical HSIs generally contain different noises of evidently distinct type and extent. While current HSI restoration methods give less consideration to such band-noise-distinctness issues, this study elaborately constructs a new HSI restoration technique, aimed at more faithfully and comprehensively taking such noise characteristics into account. Particularly, through a two-level hierarchical Dirichlet process (HDP) to model the HSI noise structure, the noise of each band is depicted by a Dirichlet process Gaussian mixture model (DP-GMM), in which its complexity can be flexibly adapted in an automatic manner. Besides, the DP-GMM of each band comes from a higher level DP-GMM that relates the noise of different bands. The variational Bayes algorithm is also designed to solve this model, and closed-form updating equations for all involved parameters are deduced. The experiment indicates that, in terms of the mean peak signal-to-noise ratio (MPSNR), the proposed method is on average 1 dB higher compared with the existing state-of-the-art methods, as well as performing better in terms of the mean structural similarity index (MSSIM) and Erreur Relative Globale Adimensionnelle de Synthèse (ERGAS).

Generating synthetic CTs from magnetic resonance images using generative adversarial networks.

While MR-only treatment planning using synthetic CTs (synCTs) offers potential for streamlining clinical workflow, a need exists for an efficient and automated synCT generation in the brain to facilitate near real-time MR-only planning. This work describes a novel method for generating brain synCTs based on generative adversarial networks (GANs), a deep learning model that trains two competing networks simultaneously, and compares it to a deep convolutional neural network (CNN). Post-Gadolinium T1-Weighted and CT-SIM images from fifteen brain cancer patients were retrospectively analyzed. The GAN model was developed to generate synCTs using T1-weighted MRI images as the input using a residual network (ResNet) as the generator. The discriminator is a CNN with five convolutional layers that classified the input image as real or synthetic. Fivefold cross-validation was performed to validate our model. GAN performance was compared to CNN based on mean absolute error (MAE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR) metrics between the synCT and CT images. GAN training took ~11h with a new case testing time of 5.7±0.6s. For GAN, MAEs between synCT and CT-SIM were 89.3±10.3 Hounsfield units (HU) and 41.9±8.6HU across the entire FOV and tissues, respectively. However, MAE in the bone and air was, on average, ~240-255HU. By comparison, the CNN model had an average full FOV MAE of 102.4±11.1HU. For GAN, the mean PSNR was 26.6±1.2 and SSIM was 0.83±0.03. GAN synCTs preserved details better than CNN, and regions of abnormal anatomy were well represented on GAN synCTs. We developed and validated a GAN model using a single T1-weighted MR image as the input that generates robust, high quality synCTs in seconds. Our method offers strong potential for supporting near real-time MR-only treatment planning in the brain.

Improving Image Quality of Cone-Beam CT Using Alternating Regression Forest.

We propose a CBCT image quality improvement method based on anatomic signature and auto-context alternating regression forest. Patient-specific anatomical features are extracted from the aligned training images and served as signatures for each voxel. The most relevant and informative features are identified to train regression forest. The well-trained regression forest is used to correct the CBCT of a new patient. This proposed algorithm was evaluated using 10 patients' data with CBCT and CT images. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR) and normalized cross correlation (NCC) indexes were used to quantify the correction accuracy of the proposed algorithm. The mean MAE, PSNR and NCC between corrected CBCT and ground truth CT were 16.66HU, 37.28dB and 0.98, which demonstrated the CBCT correction accuracy of the proposed learning-based method. We have developed a learning-based method and demonstrated that this method could significantly improve CBCT image quality. The proposed method has great potential in improving CBCT image quality to a level close to planning CT, therefore, allowing its quantitative use in CBCT-guided adaptive radiotherapy.

Efficient document-image super-resolution using convolutional neural network

Experiments performed by us using optical character recognizers (OCRs) show that the character level accuracy of the OCR reduces significantly with decrease in the spatial resolution of document images. There are real life scenarios, where high-resolution (HR) images are not available, where it is desirable to enhance the resolution of the low-resolution (LR) document image. In this paper, our objective is to construct a HR image, given a single LR binary image. The works reported in the literature mostly deal with super-resolution of natural images, whereas we try to overcome the spatial resolution problem in document images. We have trained and obtained a novel convolutional model based on neural networks, which achieves significant improvement in terms of the peak-signal-to-noise ratio (PSNR) of the reconstructed HR images. Using parametric rectified linear units, mean PSNR improvements of 2.32, 4.38, 6.43 and 8.92 dB have been achieved over those of LR input images of 50, 75, 100 and 150 dots per inch (dpi) resolution and average word level accuracy of almost 43%, 45% and 57% on 75 dpi Tamil, English and Kannada images, respectively.

A Leaning-Based Method to Improve Cone Beam CT Image Quality for Adaptive Radiation Therapy

Quantitative Cone Bean CT (CBCT) imaging is on increasing demand for precise image-guided radiation therapy (RT) because it provides a foundation for advanced image-guided techniques, including accurate treatment setup, online tumor delineation, and patient dose calculation. With more precise treatment monitoring from CBCT, dose delivery errors can be significantly reduced in each fraction or compensated for in subsequent fractions using adaptive RT. However, the current CBCT has severe artifacts mainly due to scatter contamination and its current clinical application is therefore limited to patient setup based only on bony structures. This study’s purpose is to develop a learning-based approach to improve CBCT image quality for quantitative analysis during adaptive RT. The first step is to build a set of paired training images including planning CT and CBCT. For each pair, the planning CT is used as the regression target of the CBCT. We then remove the uninformative regions, reduce noise, and perform an alignment between CT and CBCT. The proposed correction algorithm consists of two major stages: the training stage and the prediction stage. During the training stage, patch-based anatomical features are extracted from the registered training images with patient-specific information, and the most robust and informative CT-CBCT features are identified by feature selection to train random forests. During the correction stage, we extract the selected features from the new (target) CBCT and feed them into the well-trained forests to predict the corrected CBCT. This prediction-based correction algorithm was tested with brain CBCT and CT images of 9 patients. We performed leave-one-out cross-validation method to evaluate the proposed prediction-based correction algorithm. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and feature similarity (FSIM) indexes were used to quantify the differences between the corrected CBCT and planning CT. For 9 patients, the mean MAE, PSNR, and FSIM were 5.87±4.39, 28.47±4.71, and 0.90±0.08, respectively, which demonstrated the corrected CBCT prediction accuracy of the proposed learning-based method. To improve CBCT imaging, we have developed a novel learning-based method in which a random forest regression with a patch-based anatomical signature is used to effectively capture the relationship between the planning CT and CBCT. We have demonstrated that this method could significantly reduce scatter artifacts. The proposed method has great potential in improving CBCT image quality to a level close to planning CT, therefore allowing its quantitative use in adaptive RT.

Comparative analysis of different methods for image enhancement

Image enhancement technology plays a very important role to improve image quality in image processing. By enhancing some information and restraining other information selectively, it can improve image visual effect. The objective of this work is to implement the image enhancement to gray scale images using different techniques. After the fundamental methods of image enhancement processing are demonstrated, image enhancement algorithms based on space and frequency domains are systematically investigated and compared. The advantage and defect of the above-mentioned algorithms are analyzed. The algorithms of wavelet based image enhancement are also deduced and generalized. Wavelet transform modulus maxima (WTMM) is a method for detecting the fractal dimension of a signal, it is well used for image enhancement. The image techniques are compared by using the mean (µ), standard deviation (σ), mean square error (MSE) and PSNR (peak signal to noise ratio). A group of experimental results demonstrate that the image enhancement algorithm based on wavelet transform is effective for image de-noising and enhancement. Wavelet transform modulus maxima method is one of the best methods for image enhancement.

New watermark-removal method based on Eigen-image energy

Most watermark-removal methods treat watermarks as noise and apply denoising approaches to remove them. However, denoising methods remove not only this watermark energy, but also some of the energy of the original image. A trade-off therefore exists: if not enough of the watermark is removed, then it will still be detected by probabilistic methods, but if too much is removed, the image quality will be noticeably poor. To solve this problem, the relationships among the energies of the original image, the watermark and the watermarked image are initially determined using stochastic models. Then the energy of the watermark is estimated using just-noticeable-distortion (JND). Finally, the watermark energy is removed from the watermarked image using the energy distribution of its Eigen-images. The experimental results show that the proposed approach yields a mean peak signal-to-noise ratio (PSNR) of the predicted images that is 2.2 dB higher than that obtained using the adaptive Wiener filter, and a mean normalised correlation (NC) value of the extracted watermarks that is 0.27 lower than that obtained using the adaptive Wiener filter. In removing watermark energy from 100 randomly selected watermarked images in which watermarks were embedded using the ‘broken arrows (BA)’ algorithm proposed for the second breaking our watermarking system (BOWS-2) contest, the mean PSNR of 100 predicted images is 24.1 dB and the proposed approach successfully removed watermarks from 90 of these images. This result exceeds the minimum requirement of PSNR 20 dB for the BOWS-2 contest. Clearly, the proposed approach is a very effective watermark-removal approach for removing watermarks.