High-quality Recovery Research Articles

Self-Embedding Authentication Watermarking with Effective Tampered Location Detection and High-Quality Image Recovery

Recently, sensor networks have emerged as a high-impact research area, and a number of high profile applications have been proposed. Although significant progress has already been made on securing basic network protocols, additional research is needed to produce techniques and methods for protecting canonical tasks in wireless sensor networks. In this paper, we propose an effective self-embedding authentication watermarking method for tampered location detection and image recovery. The proposed detection method is classified into block-wise and pixel-wise. In block-wise detection, if the size of the block is small, the false positive rate (FPR) will be low. In pixel-wise detection, when the tampered pixels are detected, only the corresponding pixel area is marked. Therefore, the FPR will be lower than that of the block-wise detection. The experimental results demonstrate that the proposed method was effective, and accurate tamper detection and high-quality recovery can be realized even in highly tampered images.

Image compressed sensing recovery via nonconvex garrote regularization

Sparsity inducing model is one of the most important components of image compressed sensing (CS) recovery methods. These models are built on the image prior knowledge. The model which can reflect the image priors appropriately, yields high quality recovery results. Recent studies have shown that nonlocal low-rank prior based models often lead to superior results in CS recovery. The rank regularization problem resulting from these models is an NP-hard problem and how to solve it has a great impact on the recovery results. In this paper, we propose a new CS recovery method via nonconvex Garrote regularization (NGR) toward better exploiting the nonlocal low-rank prior. In the proposed CS-NGR method, the nonconvex Garrote function is introduced as a suitable surrogate for the rank function. To solve the resulting minimization problem efficiently, we employ the alternating direction method and so-called Garrote singular value shrinkage (GSVS) technique. Extensive experimental results show effectiveness of the proposed method compared with the state-of-the-arts methods in CS image recovery.

Dictionary learning sparse-sampling reconstruction method for in-vivo 3D photoacoustic computed tomography.

The sparse transforms currently used in the model-based reconstruction method for photoacoustic computed tomography (PACT) are predefined and they typically cannot capture the underlying features of the specific data sets adequately, thus limiting the high-quality recovery of photoacoustic images. In this work, we present an advanced reconstruction model using the K-VSD dictionary learning technique and present the in vivo results after adapting the model into the 3D PACT system. The in vivo experiments were performed on an IRB approved human hand and two rats. When compared to the traditional sparse transform, experimental results using our proposed method improved accuracy and contrast to noise ration of the reconstructed photoacoustic images, on average, by 3.7 and 1.8 times in the case of 50% sparse-sampling rate, respectively. We also compared the performance of our algorithm against other techniques, and imaging speed was 60% faster than other approaches. Our system would require sparse-transducer array and lower number of data acquisition hardware (DAQs) potentially reducing the cost of the system. Thus, our work provides a new way for reconstructing photoacoustic images, and it would enable the development of new high-speed low-cost 3D PACT for various biomedical applications.

Single-shot large field of view imaging with scattering media by spatial demultiplexing.

Benefiting from the memory effect (ME) for speckle intensity correlations, only one single-shot speckle pattern can be used for the high-quality recovery of objects. However, ME gives a restriction to the field of view (FOV) for imaging with scattering media. Objects beyond the ME region cannot be recovered and produce unwanted speckle patterns, which cause reduction in the speckle contrast and recovery quality. Nevertheless, all the spatial information from a large object is embedded in a single speckle image. Here, we extract the spatial information from these unavoidable speckle patterns and enlarge the FOV of the imaging system with scattering media. Regional point spreading functions, which are fixed and only need to be recorded once for all-time use, are employed to recover corresponding spatial regions of an object by deconvolution. Then, an automatic weighted averaging in an iterative process is performed to obtain the object with significantly enlarged FOV. Our results present an important advancement of imaging techniques with strongly scattering media.

Method for recovering soft X-ray emission spectra of plasmas from spectrograms recorded with a transmission diffraction grating

An iterative method was developed for recovering soft X-ray emission spectra from spectrograms obtained using a transmission diffraction grating. The method was compared with another one that was previously used. In the case of a linear radiation detector, both these methods are almost equivalent. In the case of a nonlinear detector, the method used previously could not be used, whereas the new method provides high-quality recovery of X-ray spectra.

Pulsed-high Intensity Focused Ultrasound Therapy of Rabbit Middle Cerebral Artery Thromboembolism Accompanied by Anticoagulant Nanoparticles Administration

One important challenge in image compressed sensing (CS) recovery methods is to develop a sparsity inducing model which can reflect the image priors appropriately and hence yields high quality recovery results. Recent advances have suggested that group sparse representation based models, which exploit nonlocal self-similarity prior, lead to superior results in image CS recovery. In this paper, we propose a CS recovery method via group sparse representation with nonconvex LLp norm regularization (GSR-LLp). In the proposed method, nonconvex LLp norm with (0<p≤1) is introduced as a new sparsity metric to better promote the sparsity of the group coefficients, rather than the l0 norm. Furthermore, the principle component analysis (PCA) is utilized to learn an adaptive orthogonal dictionary for each group. To solve the GSR-driven LLp minimization problem, an efficient algorithm based on split Bregman framework and Majorization–Minimization (MM) algorithm is developed. Moreover, the proposed model is combined with a robust M-estimate to cope with the case where measurements are corrupted by impulsive noise. In this case, we substitute the l2 norm data fidelity with Welch m-estimate which has shown the advantage of robustness against heavy-tailed impulsive noise. We develop an efficient scheme based on split Bregman framework and half-quadratic (HQ) theory to solve the resulting optimization problem (called as RGSR-LLp). Extensive experimental results show effectiveness of the proposed methods compared with the state-of-the-arts methods in CS image recovery.

Accurate blind deblurring using salientpatch-based prior for large-size images

The full-image based kernel estimation strategy is usually susceptible by the smooth and fine-scale background regions impacting and it is time-consuming for large-size image deblurring. Since not all the pixels in the blurred image are informative and it is frequent to restore human-interested objects in the foreground rather than background, we propose a novel concept “SalientPatch” to denote informative regions for better blur kernel estimation without user guidance by computing three cues (objectness probability, structure richness and local contrast). Although these cues are not new, it is innovative to integrate and complement each other in motion blur restoration. Experiments demonstrate that our SalientPatch-based deblurring algorithm can significantly speed up the kernel estimation and guarantee high-quality recovery for large-size blurry images as well.

Harmonics extraction based speech recovery for underdetermined mixing systems

It is an intractable task to achieve high-efficiency and high-quality speech recovery for the existing underdetermined systems. To solve this problem, this paper proposes a harmonics extraction based underdetermined speech recovery algorithm, which consists of 4 stages. In the 1st stage, spectrum correction technique is adopted to extract the harmonic components from the mixtures’ short time Fourier transform (STFT); In the 2nd stage, a phase-coherence criterion is applied on these harmonic components to identify the single source components; In the 3rd stage, these single source patterns are further categorized into multiple groups by means of the adaptive k-means clustering, from which the mixing matrix is estimated; In the last stage, this estimated matrix is further combined with the subspace projection algorithm, which resultantly yields the final source recovery. The high efficiency lies in that the harmonics extraction is properly combined with single source component identification. The speech recovery experiment demonstrated that, compared to the original subspace projection algorithm, the proposed method can acquire a higher recovery quality, which presents a potential application in other harmonic related fields.

A comparative analysis of heat recovery and product stabilization in fluid-bed and ablative pyrolysis systems

Nearly all existing commercial pyrolysis technologies employ single-step rapid condensation of vapours from 500 oC to 50 oC using sprays of cold bio-oil or liquid hydrocarbon as a quench fluid. This approach produces raw bio-oil, a non-homogenous mixture of hundreds of oxygenated organic compounds including organic acids and water. Single-step quench also results in loss of high quality heat to the ambient. In this work, a novel 3-stage fractional condensation approach has been proposed. The intent is to produce targeted stable products for value added applications as well to enhance the overall efficiency of pyrolysis processes. The first phase of this research involved modelling and simulation of staged condensation of pyrolysis vapours using Pro/2 process software. A comprehensive pyrolysis model with 13 representative compounds was developed and validated. The Pro/2 model is able to simulate complex condensation of lignin and sugar fractions at high temperatures. Multiple cases involving staged condensation in ablative and fluid bed pyrolysis systems were investigated. In each case, there was a trade-off between high-quality heat recovery and early separation of lignin and sugars from organic acids. Results demonstrated that dew point depression adds additional complexity and limits heat recovery. However, judicious selection of condenser temperatures offers opportunity for early isolation of sugars and lignin from acids, thereby improving product stability.

Block Truncation Coding based effective watermarking scheme for image authentication with recovery capability

Due to the advancement of photo-editing software, powerful computers and high resolution capturing devices, it has become tough to prevent the digital image from tampering. So, in these days just by looking a digital image we cannot say whether it is a genuine or not. This is why digital image authentication, as well as restoration, has become the essential issues, especially when it is utilized in medical science, evidence of court, and forensic science. This paper proposes an effective self-embedding fragile watermarking technique for the digital image authentication as well as recovery. The watermark is generated by quantization, and block truncation coding (BTC) of each 2 × 2 non-overlapping block and embedded in three least significant bits (LSBs) of the corresponding mapped block. The recovery bits are derived from most significant bits (MSBs) of the host image, and the authentication bits are derived from recovery bits, the spatial location of pixels and watermark keys. Even if tempering rate is 50%, the reconstruction of tampered image is achieved with high peak signal-to-noise ratio (PSNR) and normalized correlation coefficient (NCC). The experimental results demonstrate that the proposed scheme not only outperforms high-quality recovery fidelity but also negotiate the blocking artifacts additionally it improves the accuracy of tamper localization due to the use of very small size blocks.

Pioneer craniotomy at Babcock University Teaching Hospital: Anaesthetic management

Glioblastoma multiforme is the highest grade glial tumour and most common in the astrocytic line. This usually requires craniotomy for tumour biopsy, relief of intracranial pressure and reduction in tumour mass as much as possible. In the anaesthetic management of patient for craniotomy, emphasis should be on the provision of safe and optimal preoperative conditions, preservation of neurocognitive functions and a rapid high quality recovery. Maintenance of adequate cerebral perfusion pressure (CPP), normal intra-cranial pressure (ICP) and cerebral oxygenation (CMRO2) is the mainstay of neuro-anaesthesia and requires a thorough understanding of the physiology of the central nervous system (CNS). We report a case of a 59 year old man with glioblastoma multiforme and features of raised intracranial pressure that successfully underwent craniotomy and decompression of the tumour.

Optical Cryptography-Based Temporal Ghost Imaging With Chaotic Laser

In this letter, compressive temporal ghost imaging with chaotic laser is applied to optical encryption with good time-controllable random characteristics and a second-order time correlation property. The security, feasibility, and applicability of our method are verified on the basis of the research on the encryption mechanism. The experiment suggests the method can obtain high-quality recovery signal, resist noise attack, and apply to different kinds of signals. This technique can be immediately applied to encryption and telecommunication with the advantages of high security, wide applicability, and high quality of reconstructed information.

Fragile image watermarking with pixel-wise recovery based on overlapping embedding strategy

In this paper, we propose a new fragile watermarking scheme with high-quality recovery capability based on overlapping embedding strategy. The block-wise mechanism for tampering localization and the pixel-wise mechanism for content recovery are collaborated in the proposed scheme. With the assist of interleaving operation, reference bits are derived from mean value of each overlapping block, and then are dispersedly embedded into 1 LSB or 2 LSB layers of the image, corresponding to horizontal-vertical mode and diagonal mode, respectively. Authentication bits are hidden into adaptive LSB layers of the central pixel for each block according to block complexity. On the receiver side, after locating tampered blocks and reconstructing mean-value bits, according to the types of tampered pixels in each overlapping block, three pixel-wise manners are exploited for tampering recovery based on different neighboring blocks. Even if the tampered area is extensive, the proposed scheme can achieve better quality of recovered image compared with some of state-of-the-art schemes.

Effect of spatial spectrum overlap on Fourier ptychographic microscopy

Fourier ptychographic microscopy (FPM) is a newly developed imaging technique which stands out by virtue of its high-resolution and wide FOV. It improves a microscope’s imaging performance beyond the diffraction limit of the employed optical components by illuminating the sample with oblique waves of different incident angles, similar to the concept of synthetic aperture. We propose to use an objective lens with high-NA to generate oblique illuminating waves in FPM. We demonstrate utilizing an objective lens with higher NA to illuminate the sample leads to better resolution by simulations, in which a resolution of 0.28[Formula: see text][Formula: see text]m is achieved by using a high-NA illuminating objective lens (NA[Formula: see text][Formula: see text]) and a low-NA collecting objective lens (NA[Formula: see text][Formula: see text]) in coherent imaging ([Formula: see text][Formula: see text]nm). We then deeply study FPM’s exact relevance of convergence speed to spatial spectrum overlap in frequency domain. The simulation results show that an overlap of about 60% is the optimal choice to acquire a high-quality recovery (520*520 pixels) with about 2 min’s computing time. In addition, we testify the robustness of the algorithm of FPM to additive noises and its suitability for phase objects, which further proves FPM’s potential application in biomedical imaging.

Preliminary study on preparation of ammonium metatungstate from ammonium tungstate solutions using bipolar membrane electrodialysis

A novel process for the preparation of ammonium metatungstate (AMT) from industrial ammonium tungstate solutions using bipolar membrane electrodialysis (BMED) was developed. The preliminary study results showed that the BMED process can be used to effectively prepare the AMT solution from the (NH4)2WO4 solution with the current efficiency of 73.8%. The direct recovery of WO3 reached 99.5% with the power consumption only 864.3kWh/t WO3. The obtained AMT solution can be directly used to produce the AMT crystal. The solubility of the final AMT product reached 650g AMT per 100g H2O at 25°C, which meets the Chinese National Standard on the preparation of petrochemical hydrodesulfurization catalysts. The new process exhibits significant advantages including short flow sheet, low energy consumption, high recovery of WO3 and high quality of product, and environmental friendliness.

A hybrid evolutionary algorithm for multiobjective sparse reconstruction

Sparse reconstruction (SR) algorithms are widely used in acquiring high-quality recovery results in compressed sensing. Existing algorithms solve SR problem by combining two contradictory objectives (measurement error and sparsity) using a regularizing coefficient. However, this coefficient is hard to determine and has a large impact on recovery quality. To address this concern, this paper converts the traditional SR problem to a multiobjective SR problem which tackles the two objectives simultaneously. A hybrid evolutionary paradigm is proposed, in which differential evolution is employed and adaptively configured for exploration and a local search operator is designed for exploitation. Another contribution is that the traditional linearized Bregman method is improved and used as the local search operator to increase the exploitation capability. Numerical simulations validate the effectiveness and competitiveness of the proposed hybrid evolutionary algorithm with LB-based local search in comparison with other algorithms.

Restoration-Multiple Removing of Noise under Water Images using Multidirectional Filtering Techniques

2 Abstract: Image restoration one part is the Denoising which plays important tasks in image processing. Despite the significant research conducted on this topic, the development of efficient denoising methods is still a compelling challenge. Image denoising is an essential requirement of image processing. The images contain strongly oriented harmonics and edge discontinuities. Wavelets, which are localized and multiscaled, do better denoising in single dimension using multiple local thresholding technique. Filter based denoising and reconstruction exhibit higher quality recovery of edges and curvilinear features. This thresholding scheme denoises images embedded in Speckle noise. The experiment shows denoising using Filters such as Wiener, Median, Wavelet Transform , Bayes Shrink and our proposed technique as (median and bayes Shrink wavelet) to outperforms in terms of PSNR(peak signal-to-noise ratio) , MSE (mean square Error), Elapsed time and Coc (Coefficient of Correlation ), but also in better visual appearance of the resulting images. In this thesis, we will study and investigate the application of using best filters to remove noise using our proposed method Median with Bayes shrink wavelet with soft thresholding for denoising techniques to remove multiple noises from under water images. In this, Gaussian, Poisson, Salt & pepper, Speckle is used for restoration. Our Technique, works best for all types of noises but speckle is better restored as denoised by wavelet based only technique.

Sub-Nyquist sampling and detection in Costas coded pulse compression radars

Modern pulse compression radar involves digital signal processing of high bandwidth pulses modulated with different coding schemes. One of the limiting factors in the radar’s design to achieve desired target range and resolution is the need of high rate analog-to-digital (A/D) conversion fulfilling the Nyquist sampling criteria. The high sampling rates necessitate huge storage capacity, more power consumption, and extra processing requirement. We introduce a new approach to sample wideband radar waveform modulated with Costas sequence at a sub-Nyquist rate based upon the concept of compressive sensing (CS). Sub-Nyquist measurements of Costas sequence waveform are performed in an analog-to-information (A/I) converter based upon random demodulation replacing traditional A/D converter. The novel work presents an 8-order Costas coded waveform with sub-Nyquist sampling and its reconstruction. The reconstructed waveform is compared with the conventionally sampled signal and depicts high-quality signal recovery from sub-Nyquist sampled signal. Furthermore, performance of CS-based detections after reconstruction are evaluated in terms of receiver operating characteristic (ROC) curves and compared with conventional Nyquist-rate matched filtering scheme.

Valorization of Bambara groundnut shell via intermediate pyrolysis: Products distribution and characterization

This study provides first report on thermochemical conversion of residue from one of the underutilized crops, Bambara groundnut. Shells from two Bambara groundnut landraces KARO and EX-SOKOTO were used. Pyrolysis was conducted in a vertical fixed bed reactor at 500, 550, 600 and 650 °C; 50 °C/min heating rate and 5 L/min nitrogen flow rate. The report gives experimental results on characteristic of the feedstock, impact of temperature on the pyrolysis product distribution (bio-oil, bio-char and non-condensable gas). It evaluates the chemical and physicochemical properties of bio-oil, characteristics of bio-char and composition of the non-condensable gas using standard analytical techniques. KARO shell produced more bio-oil and was maximum at 600 °C (37.21 wt%) compared to EX-SOKOTO with the highest bio-oil yield of 32.79 wt% under the same condition. Two-phase bio-oil (organic and aqueous) was collected and analyzed. The organic phase from both feedstocks was made up of benzene derivatives which can be used as a precursor for quality biofuel production while the aqueous from KARO consisted sugars and other valuable chemicals compared to the aqueous phase from EX-SOKOTO which comprised of acids, ketones, aldehydes and phenols. Characteristics of bio-char and composition of the non-condensable were also determined. The results show that bio-char is rich in carbon and some minerals which can be utilized either as a solid fuel or source of bio-fertilizer. The non-condensable gas was made up of methane, hydrogen, carbon monoxide and carbon dioxide, which can be recycled to the reactor as a carrier gas. This study demonstrated recovery of high quality fuel precursor and other valuable materials from Bambara groundnut shell.

Monte Carlo data-driven tight frame for seismic data recovery

Seismic data denoising and interpolation are essential preprocessing steps in any seismic data processing chain. Sparse transforms with a fixed basis are often used in these two steps. Recently, we have developed an adaptive learning method, the data-driven tight frame (DDTF) method, for seismic data denoising and interpolation. With its adaptability to seismic data, the DDTF method achieves high-quality recovery. For 2D seismic data, the DDTF method is much more efficient than traditional dictionary learning methods. But for 3D or 5D seismic data, the DDTF method results in a high computational expense. The motivation behind this work is to accelerate the filter bank training process in DDTF, while doing less damage to the recovery quality. The most frequently used method involves only a randomly selective subset of the training set. However, this random selection method uses no prior information of the data. We have designed a new patch selection method for DDTF seismic data recovery. We suppose that patches with higher variance contain more information related to complex structures, and should be selected into the training set with higher probability. First, we calculate the variance of all available patches. Then for each patch, a uniformly distributed random number is generated and the patch is preserved if its variance is greater than the random number. Finally, all selected patches are used for filter bank training. We call this procedure the Monte Carlo DDTF method. We have tested the trained filter bank on seismic data denoising and interpolation. Numerical results using this Monte Carlo DDTF method surpass random or regular patch selection DDTF when the sizes of the training sets are the same. We have also used state-of-the-art methods based on the curvelet transform, block matching 4D, and multichannel singular spectrum analysis as comparisons when dealing with field data.