Signal-to-noise ratio of lensless ghost interference with thermal incoherent light

Factors influencing the signal-to-noise ratio （SNR） of lensless ghost interference with thermal incoherent light are investigated. Our result shows that the SNR of lensless ghost interference is related to the transverse length of the object, the position of the object in the imaging system and the transverse size of the light source. Furthermore, the effects of these factors on the SNR are discussed in detail by numerical simulations.

Signal-to-noise ratio comparison of angular signal radiography and phase stepping method

Grating-based x-ray phase contrast imaging has the potential to be applied in future medical applications as it is compatible with both laboratory and synchrotron source. However, information retrieval methods are important because acquisition speed, scanning mode, image quality, and radiation dose depend on them. Phase-stepping （PS） is a widely used method to retrieve information, while angular signal radiography （ASR） is a newly established method. In this manuscript, signal-to-noise ratios （SNRs） of ASR are compared with that of PS. Numerical experiments are performed to validate theoretical results. SNRs comparison shows that for refraction and scattering images ASR has higher SNR than PS method, while for absorption image both methods have same SNR. Therefore, our conclusions would have guideline in future preclinical and clinical applications.

Denoising Letter Images from Scanned Invoices Using Stacked Autoencoders

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

An Improved Image Denoising Method Based on Wavelet Thresholding

VisuShrink, ModineighShrink and NeighShrink are efficient image denoising algorithms based on the discrete wavelet transform (DWT). These methods have disadvantage of using a suboptimal universal threshold and identical neighbouring window size in all wavelet subbands. In this paper, an improved method is proposed, that determines a threshold as well as neighbouring window size for every subband using its lengths. Our experimental results illustrate that the proposed approach is better than the existing ones, i.e., NeighShrink, ModineighShrink and VisuShrink in terms of peak signal-to-noise ratio (PSNR) i.e. visual quality of the image.

A New Image Denoising Scheme Using Soft-Thresholding

The VisuShrink is one of the important image denoising methods. It however does not provide good quality of image due to removing too many coefficients especially using soft-thresholding technique. This paper proposes a new image denoising scheme using wavelet transformation. In this paper, we modify the coefficients using soft-thresholding method to enhance the visual quality of noisy image. The experimental results show that our proposed scheme has better performance than the VisuShrink in terms of peak signal-to-noise ratio (PSNR) i.e., visual quality of the image.

THE METHOD OF SEPARATING HARMONIC SIGNALS FROM MULTIPLICATIVE AND ADDITIVE NOISES

This paper focuses on the extraction of a harmonic signal from multiplicative and additive noises. A method is proposed in two stages： （1） to square the original discrete time series, which includes both signals and noises, and form a new time series. By this means, the multiplicative noise is converted to additive noise; and （2） to filter out the noise by using existing noise removal schemes. With a large amount of simulation, experimental results demonstrated the efficiency and effectiveness of this newly developed method in terms of Signal-to-Noise Ratio （SNR） and other criteria. Prom the experiment, it is also found that： the two kinds of noises affect the SNR differently. In general, the SNR is not influenced by multiplicative Gaussian noise regardless of its variance. However, if both kinds of noise exist, the SNR decreases with the incensement of the Variance of Additive Noise to Multiplicative Noise Ratio （VAMNR）. This analysis is also supported by simulation work.

New Wavelet Thresholding Algorithm in Dropping Ambient Noise from Underwater Acoustic Signals

Underwater Wireless Communication, largely dependent on the acoustic communication between the machines, is largely affected by various types of noise in the shallow and deep water. However ambient noise which is due to multiple sources (e.g. shipping, wind) and no one source dominates. Ambient noise masks the acoustic signal to a large extent. Hence today it has drawn the attention of the experts to reduce its effect on the received signal. This paper discusses ambient noise problem and devises a new wavelet thresholding method to reduce its effect. Afterwards a comparative study on statistical parameters is shown to prove the efficiency of the devised method.

Noise properties of multi-combination information in x-ray grating-based phase-contrast imaging

Grating-based x-ray phase contrast imaging has attracted increasing interest in recent decades as multimodal and laboratory source usable method.Specific efforts have been focused on establishing a new extraction method to perform practical applications.In this work,noise properties of multi-combination information of newly established information extraction method,so-called angular signal radiography method,are investigated to provide guidelines for targeted and specific applications.The results show that how multi-combination of images can be used in targeted practical applications to obtain a high-quality image in terms of signal-to-noise ratio.Our conclusions can also hold true for upcoming targeted practical applications such as biomedical imaging,non-destructive imaging,and materials science.

Row-action SAGE algorithm for PET image reconstruction

The one-block version of ordered subsets (OS) techniques was used to accelerate the convergent rate of the space-alternating generalized expectation-maximization (SAGE) algorithm. The new row-action SAGE (RA-SAGE) algorithm processed projections in sequentially orthogonal order which reduced the dependency among the projections and speeds up the convergences. Additionally, the over-relaxation parameter in the direction defined by the RA-SAGE algorithm was also applied to obtain fast convergence to a globally maximum likelihood (ML) solution. In experiments, the RA-SAGE algorithm and the classical SAGE algorithm were compared in the application to positron emission tomography (PET) image reconstruction. Simulation results showed that RA-SAGE had better performance than SAGE in both convergence and image quality.

Application Research of Image Compression Technology in Monitoring System of Rape Crop in Areas of Qinling Mountains

[Objective] The aim was to present a proposal about a new image compression technology, in order to make the image be able to be stored in a smaller space and be transmitted with smaller bit rate on the premise of guaranteeing image quality in the rape crop monitoring system in Qinling Mountains. [Method] In the proposal, the color image was divided into brightness images with three fundamental colors, followed by sub-image division and DCT treatment. Then, coefficients of transform domain were quantized, and encoded and compressed as per Huffman coding. Finally, decompression was conducted through inverse process and decompressed images were matched. [Result] The simulation results show that when compression ratio of the color image of rape crops was 11.972 3∶1, human can not distinguish the differences between the decompressed images and the source images with naked eyes; when ratio was as high as 53.565 6∶1, PSNR was still above 30 dD,encoding efficiency achieved over 0.78 and redundancy was less than 0.22. [Conclusion] The results indicate that the proposed color image compression technology can achieve higher compression ratio on the premise of good image quality. In addition, image encoding quality and decompressed images achieved better results, which fully met requirement of image storage and transmission in monitoring system of rape crop in the Qinling Mountains.

A fast decoupled ISAR high-resolution imaging method using structural sparse information under low SNR

Inverse synthetic aperture radar (ISAR) image can be represented and reconstructed by sparse recovery (SR) approaches. However, the existing SR algorithms, which are used for ISAR imaging, have suffered from high computational cost and poor imaging quality under a low signal to noise ratio (SNR) condition. This paper proposes a fast decoupled ISAR imaging method by exploiting the inherent structural sparse information of the targets. Firstly, the ISAR imaging problem is decoupled into two sub-problems. One is range direction imaging and the other is azimuth direction focusing. Secondly, an efficient two-stage SR method is proposed to obtain higher resolution range profiles by using jointly sparse information. Finally, the residual linear Bregman iteration via fast Fourier transforms (RLBI-FFT) is proposed to perform the azimuth focusing on low SNR efficiently. Theoretical analysis and simulation results show that the proposed method has better performence to efficiently implement higher-resolution ISAR imaging under the low SNR condition.

An effective fractal image compression algorithm based on plane fitting

A new method using plane fitting to decide whether a domain block is similar enough to a given range block is proposed in this paper. First, three coefficients are computed for describing each range and domain block. Then, the best-matched one for every range block is obtained by analysing the relation between their coefficients. Experimental results show that the proposed method can shorten encoding time markedly, while the retrieved image quality is still acceptable. In the decoding step, a kind of simple line fitting on block boundaries is used to reduce blocking effects. At the same time, the proposed method can also achieve a high compression ratio.

Long term integration of radar signals with unknown Doppler shift for ubiquitous radar

Ubiquitous radar is a new radar system that provides continuous and uninterrupted multifunction capability within a coverage volume. Continuous coverage from close-in ＂pop-up＂ targets in clutter to long-range targets impacts selection of waveform parameters. The coherent processing interval （CPI） must be long enough to achieve a certain signal-to-noise ratio （SNR） that ensures the efficiency of detection. The condition of detection in the case of low SNR is analyzed, and three different cases that would occur during integration are discussed and a method to determine the CPI is presented. The simulation results show that targets detection with SNR as low as -26 dB in the experimental system can possibly determine the CPI.

A NOVEL METHOD FOR ESTIMATING THE SNR OF UNKNOWN EMITTER SIGNAL

It is important to estimate the Signal-to-Noise Ratio(SNR) of unknown emitter signal accurately.In order to resolve the disadvantages of present algorithm,a novel method is proposed in this letter.We extract and normalize the information of zero frequency of received signal by the Wigner-Vile Distribution(WVD) transformation and then get the approximate power of original signal by mathematic transformation,at last,we get the estimate value of SNR by the known account formula of SNR.Simulation results show that it is correct and feasible.

Nonsaturation Throughput Enhancement of IEEE 802.11b Distributed Coordination Function for Heterogeneous Traffic under Noisy Environment

In this paper, we propose a mechanism named modified backoff （MB） mechanism to decrease the channel idle time in IEEE 802.11 distributed coordination function （DCF）. In the noisy channel, when signal-to-noise ratio （SNR） is low, applying this mechanism in DCF greatly improves the throughput and lowers the channel idle time. This paper presents an analytical model for the performance study of IEEE 802.11 MB-DCF for nonsaturated heterogeneous traffic in the presence of transmission errors. First, we introduce the MB-DCF and compare its performance to IEEE 802.11 DCF with binary exponential backoff （BEB）. The IEEE 802.11 DCF with BEB mechanism suffers from more channel idle time under low SNR. The MB-DCF ensures high throughput and low packet delay by reducing the channel idle time under the low traffic in the network. However, to the best of the authors＇ knowledge, there are no previous works that enhance the performance of the DCF under imperfect wireless channel. We show through analysis that the proposed mechanism greatly outperforms the original IEEE 802.11 DCF in the imperfect channel condition. The effectiveness of physical and link layer parameters on throughput performance is explored. We also present a throughput investigation of the heterogeneous traffic for different radio conditions.

EFFICIENT IMAGE TRANSMISSION SCHEME IN FMT SYSTEM

An efficient image transmission scheme is proposed based on byte partition and adaptive sub-channel distribution technique in Filtered MultiTone (FMT) system over frequency selective slow fading channel. According to the simulation results and analysis of a typical image with matlab, improvement in Peak Signal to Noise Ratio (PSNR) of the received image and low complexity for equalization is demonstrated remarkably. Comparing with no adaptive and no actual channel equalization scheme, the proposed scheme saves over 6 dB when PSNR=40 dB.

An Adaptive Method of Image CompressionBased on Wavelet Analysis

Through research for image compression based on wavelet analysis in recent years, we put forward an adaptive wavelet decomposition strategy. Whether sub-images are to be decomposed or not are decided by their energy defined by certain criterion. Then we derive the adaptive wavelet decomposition tree (AWDT) and the way of adjustable compression ratio. According to the feature of AWDT, this paper also deals with the strategies which are used to handle different sub-images in the procedure of quantification and coding of the wavelet coefficients. Through experiments, not only the algorithm in the paper can adapt to various images, but also the quality of recovered image is improved though compression ratio is higher and adjustable. When their compression ratios are near, the quality of subjective vision and PSNR of the algorithm are better than those of JPEG algorithm.

Novel Frame Shift and Integral Technique for Enhancing Low-Light-Level Moving Images

A novel frame shift and integral technique for the enhancement of low light level moving image sequence is introduced. According to the technique, motion parameters of target are measured by algorithm based on difference processing. To obtain spatial relativity, images are shifted according to the motion parameters. As a result, the processing of integral and average can be applied to images that have been shifted. The technique of frame shift and integral that includes the algorithm of motion parameter determination is discussed, experiments with low light level moving image sequences are also described. The experiment results show the effectiveness and the robustness of the parameter determination algorithm, and the improvement in the signal-to-noise ratio （SNR） of low light level moving images.

Hardware implementation of adaptive filter for noise cancellation using TMS320C6713

Daily, we experience the effects of audio noise, which contaminates the original information bearing signal with noise from its surrounding environment. This paper focuses on real-time hardware implementation of multi-tap adaptive noise cancellation （ANC） system by using the least mean square （LMS） algorithm on TMS320C6713 to remove undesired noise from a received signal for various audio related applications. Three different experiments are carried out by considering different audio inputs to test the efficiency of the designed ANC system. The ＇C＇ code implementation of LMS algorithm is introduced and simulated in code composer studio （CCS）, then realized on the digital signal processor （DSP） C6713. The 300 Hz, 500 Hz, 800 Hz, 1 kHz and 3 kHz of tone signals and male speech signal are used as the reference inputs to trace the noise of signal until it is eliminated. The performance of ANC system is studied in terms of convergence speed, order of the filter and signal-to-noise ratio （SNR）. The experimentam results demonstrate that the designed system shows a consider- able improvement in SNR.

Network Sorting Algorithm of Multi-Frequency Signal with Adaptive SNR

An signal noise ratio（ SNR） adaptive sorting algorithm using the time-frequency（ TF）sparsity of frequency-hopping（ FH） signal is proposed in this paper. Firstly,the Gabor transformation is used as TF transformation in the system and a sorting model is established under undetermined condition; then the SNR adaptive pivot threshold setting method is used to find the TF single source. The mixed matrix is estimated according to the TF matrix of single source. Lastly,signal sorting is realized through improved subspace projection combined with relative power deviation of source. Theoretical analysis and simulation results showthat this algorithm has good effectiveness and performance.