Cameraman Image Research Articles

Research on network data transmission and compression based on compression-aware model

Abstract This paper first analyzes the theory of compressive sensing for wireless sensors and constructs a mathematical model of compressive sensing. Secondly, the sparse representation and observation of the signal in the compressed sensing technique are analyzed to provide the theoretical basis for the new model of P-tensor product compressed sensing based on the digital signature encryption algorithm in the later paper. Finally, the recovery performance of the model and the encryption effect is analyzed by simulation experiments. The results show that when the CR is 0.8, the PSNR values of the Lena image, Peppers image and Cameraman image are 37.608dB, 37.32884dB and 37.3428dB, respectively. 512×512 network data fragment has the shortest encryption time of 2.0156s. This shows that the compression-aware model can guarantee network data transmission security and guarantee compression quality.

Sparse Compression-Based Image Encryption using Data Encryption Standards RC5

In this work, we have used the standard symmetric key block cipher data encryption algorithm RC5(32,16,8) for the encryption of digital greyscale images. For this, we have only considered the nonzero elements of the sparse matrix of the images which have been generated using the sparse representation technique. The security strength of the proposed technique is verified through different quality parameters, e.g. information entropy, correlation coefficients, NPCR, UACI, and NIST test. Our proposed algorithm achieved the maximum information entropy value of 7.9977 for the cameraman image and nearly zero correlation coefficient values establish the robustness of the cryptosystem. The highest NPCR and UACI values obtained in our work are 99.6916 and 33.9605, which are closer to the maximum theoretical values. The proposed technique is also compared with some of the contemporary works to validate the credibility of our proposed algorithm.

Image compression in all-optical domain using one 6×6 multimode interference coupler

We present a new method for image compression using the Haar wavelet transform (HWT) in all-optical domain. The proposed architecture is based on the optical Haar wavelet transform using only one 6×6 multimode interference (MMI) coupler. By locating the positions of input and output waveguides and optimizing design length of the MMI, the expected matrix of the Haar transform is obtained. The new hardware architecture is suitable for directly integrating with digital cameras for image processing. The processing of images therefore is at very high speed. Our method can also be applied to data compression in big data analytics. Our structure can provide a large fabrication tolerance which is compatible with the CMOS existing technology. Our simulations show that the length variation of ± 2 µm still keeps the output powers unchanged. We have simulate successfully the use of the proposed HWT to compress cameraman image with the compressed ratios of 20, 30, 50 % with MSE and PSNR from 0.1 - 0.3 and 62 – 67 dB, respectively.

Image Transmission Using SC-FDMA System Over mmWave Measured Channel at 29.5 GHz

Aims: This paper presents an investigation of image transmission using SC-FDMA system over wireless fading channels. Background: A comparison is performed between the performance of the wireless transmitted image over 3GPP LTE channel (vehicular and pederstain) and mmWaves measured channel at 29.5 GHz. Objective: One of the important benefits of this work is the real measurement obtained at 29.5 GHz under NLOS scenario using an original sounder based on the ensuring of phase coherence between the transmitter and the receiver. Methods: Cameraman image has been transmitted over the SC-FDMA with different subcarriers mapping schemes. PSNR and MSE are the metrics used to evaluate the performance of the image transmitted under Matlab simulation where their values of the received image are calculated for different SNR values. Results and Conclusion: : The simulation results show that the image transmission over mmWaves channel works and the best values of PSNR are attained beyond 12 dB and 14 dB for LSC-FDMA and ISC-FDMA respectively.

Design and Implementation of Fractional Order Integral Filter for Denoising of Echocardiographic Images

Denoising techniques demand the areas of analysis which are capable of suppressing noise and also preserving the edges and structure with respect to image quality metrics, visual quality assessment and clinical validation. Denoising is an essential step of preprocessing for improving the diagnostic value of the images. Fractional order calculus is a demanding area of research in various fields of image processing. Fractional Calculus based Denoising filter is proposed in the paper. Denoising is accomplished by the application of filter mask on each pixel of the noisy image. Fractional order integral filters are higher in accuracy in comparison to the integer-order integral filters. The adaptive fractional integral algorithm for image denoising is the thrust area of research in image processing and can prove to be more fruitful. The fractional order denoising algorithm requires structures of nxn filter masks of digital images. The algorithm is tested to both subjective and objective standards of perceiving ability of the human visual system. Simulations using MATLAB are carried out on real echocardiographic as well as standard images. The filter is tested on synthetic Images like Lena Image, Checkerboard image and cameraman image, only the synthetic images are corrupted by adding speckle noise of different noise variances. Further, the filter was tested on real echocardiographic images in different views; since there is no reference noise-free real echo image available as the gold standard for the performance verification of the proposed denoising filter which is only based on the results of standard images. The thrust area includes a faster implementation of the proposed method compared to other methods of denoising. The results obtained using the proposed filter performs better than other recently proposed methods, both in terms of quantitative and qualitative measures. Quantitative metrics being used are Peak signal to noise ratio (PSNR), Signal to noise ratio (SNR) and Mean square error (MSE). Further, the order of the filter is adaptive which is more significant for image processing like segmentation and feature extraction. The proposed denoising technique not only improves the quality of low contrast images but also effectively reduces speckle noise in echo and synthetic images.

A Singular Value Decomposition Based Low-Computational Zero-Watermark Algorithm For Digital Right Management

Digital rights management (DRM) is a systematic approach used for protecting the exclusive rights in the digital mass media. It uses a set of technologies to control doubling and reproducing exclusive rights for the digital works and software. The digital watermarking is one of the powerful technologies that play a vital role in numeral rights management. In this paper, a low-computational zero watermark (ZW) algorithm has been projected. It depends on the singular value decomposition (SVD) and implemented on standard cameraman, Barbara, Lena and living room images without attack and with various attacks. The significant feature of this algorithm is that it does not fuse any watermarking in the given source image and hence the result of the zero-watermark algorithm is looking very similar to the source image. This zero-watermark property is obtained by using SVD approach in which the ZW sequence is computed in accordance with the equivalence of prior digits of major remarkable worth in every slab. The implementation consequences shows highest similarity measures of 0.8658 for cameraman image. Further, the computational cost of the algorithm is calculated as 4.442 msec of execution time for all the images under watermark embedder and watermark extractor phases. The PSNR values are calculated for the watermarked images for testing the robustness in the algorithm that is proposed, and the observations have shown the promising results against attack.

Different applied median filter in salt and pepper noise

In this paper, we proposed a new method, Different Applied Median Filter (DAMF), to remove salt and pepper (SAP) noise at all densities. We then explained some basic notions of it. Afterwards, we compared the results of DAMF method and some other methods by using Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) for some images such as Cameraman and Lena. For example, for Cameraman image with a SAP noise ratio of 30%, PSNR and SSIM results of PSMF, DBA, MDBUTMF and NAFSM methods are 28.27/ 29.28/ 29.44/ 32.09 and 0.9044/ 0.9324/ 0.7740/ 0.9494 respectively while PSNR and SSIM results of DAMF method are 36.83 and 0.9844, respectively. We finally showed that DAMF could be successfully removed SAP noise at all densities.

Symmetric illumination in Fourier ptychography

Fourier ptychography (FP) is a newly developed imaging technology, which can reconstruct high-resolution (HR) wide-field image from a series of low-resolution (LR) images. The limitation of FP is its long acquisition and reconstruction time due to the numerous LR images that are needed and the low illumination intensity of light-emitting diodes (LEDs) which lead to long exposure time of imaging sensors. Many researches have been done to speed up FP. The available speeding-up methods with single LED illumination are still constrained by low illumination intensity of LED. Although multi-illumination methods can improve illumination intensity, they are time-consuming during spectrum decomposition. In this paper, we demonstrate a new efficient method, termed symmetric Fourier ptychography (SFP). For thin samples irrespective of phases, two center-symmetric illuminations generate the same intensity distribution, so that two center-symmetric LEDs used in FP can be lit up simultaneously and the illumination intensity is doubled. Spectra have central conjugate symmetry in Fourier domain so that only half of spectra need recovering, then, the processing time can be reduced by about 50%. Simulations are conducted with the Cameraman image as input amplitude. The LR images are generated based on the FP simulation process and then the LR images generated by LEDs from two center-symmetrical positions are summed. Furthermore, HR images are recovered by using FP reconstruction algorithms. It is found that root-mean-square-error of SFP is almost the same as that of traditional FP, which indicates that the SFP can achieve the same performance as that of traditional FP. Then, central conjugate symmetry is adopted in Fourier domain, where only half of spectra are recovered and the other half of spectra are obtained from conjugate symmetry. It proves that HR images can be recovered based on central conjugate symmetry in Fourier domain and 50% of processing time is saved. For imaging experiments of USAF target and biological samples, two LEDs of central symmetry are lit up simultaneously, and 113 LR images are gathered in contrast with 225 ones of traditional FP. It is also found that SFP can achieve the same resolution as that of the traditional FP. In the meantime, SFP can reduce about 50% LR images and save about 70% acquisition time without increasing the complexity of FP system and algorithms. In addition, SFP can be combined with other methods to further speed up the speed of FP, and its feasibility is proven by the experimental results of combination with adaptive Fourier ptychography. All results in this paper indicate that the proposed method has the potential to improve the application of FP in real-time imaging.

An alternative image quality assessment method for blurred images

This study proposes a no-reference image quality assessment method for blurred images. In this approach, first, a discrete wavelet transform was applied to the sample images and then the results were decomposed into four subbands. This was followed by the calculation of the spatial frequencies of high-high (HH2) and low-low (LL2) subbands. Then the ratio of spatial frequencies of HH2 and LL2 subbands was calculated. Information about the image quality was obtained by using this ratio, with lower values indicative of better image quality. The study aims to investigate whether the proposed method is capable of measuring the image quality. The proposed technique was tested on the standard images. Three different images were used, of which each one was distorted with the same type and amount of noise. Motion noise, blurring and sharpening was applied to distort the images. The performance of the proposed method was evaluated and compared with eight representative image quality measures. This provides a meaningful comparison across different types of image distortions. Then, the cameraman image was also blurred with two different noises: Gaussian and disk-shaped blur. The varying amount of blur was compared with Universal Image Quality Index (UIQI) values of the cameraman image. The method gives good results in different resolutions as well. Its computation is easy, independent of viewing conditions.

Split and Merge Based Quantitative Approach to Select Filter Combination for Image Segmentation

With the advent of image analysis and computation in different domains, image segmentation has emerged as the most crucial step to achieve a compact segment-based description of image scene by decomposing it into meaningful segments of similar attributes. The pre-and-post filtering operation reduces the effect of noise from the segmented image. The Cameraman image is pre-filtered using Laplacian, Median and Min filter. The Split and Merge method for Region based image segmentation which guarantees to connected regions are now applied on the filtered image. The Median, Laplacian and Sobel filter is then used to post-filter the segmented image. The PSNR and MSE values are calculated to quantitative evaluation of segmented images. The quantitative evaluation of post-filtered segmented image shows that median filter produces most effective result with lowest MSE of 84.89 dB and highest PSNR of 5.72 dB.

DIGITAL WATERMARKING WITH A NEW ALGORITHM

Everyday millions of data need to transmit through a distinct channel for various purposes; as a result there is a certain chance of third person interruption on that data. In this regards digital watermarking is one of the best solution. This paper proposes a new embedding algorithm (NEA) of digital watermarking. The algorithm is performed for digital image as data. The performance is compared for NEA and well established Cox's modified embedding algorithm. The watermarking is based on discrete wavelet transforms (DWT) and discrete cosine transforms (DCT). The acceptance of the new algorithm is measured by the two requirements of digital watermarking. One is imperceptibility of the watermarked image, measured by peak signal to noise ratio (PSNR) in dB; another one is robustness of the mark image, measured by correlation of original mark image and recovering mark image. Here a 512×512 gray scale Lena and Cameraman's image is taken as host images, and a 128×128 gray scale image is taken as mark image for 2 level of DWT. The simulation results for different attacking conditions such as salt and pepper attack, additive white Gaussian noise (AWGN) attack, jpg compression attack, gamma attack, histogram attack, cropping attack, sharpening attack etc. After different attacks the changing tendency PSNR for both algorithms are similar. But the mean square error (MSE) value of NEA is always less than Cox’s modified algorithm, which means that after embedding the changes of the host image property lower for NEA than Cox’s algorithm. From the simulation results it can be said that NEA will be a substitute of modified Cox’s algorithm with better performance.

Supervised range-constrained thresholding

A novel thresholding approach to confine the intensity frequency range of the object based on supervision is introduced. It consists of three steps. First, the region of interest (ROI) is determined in the image. Then, from the histogram of the ROI, the frequency range in which the proportion of the background to the ROI varies is estimated through supervision. Finally, the threshold is determined by minimizing the classification error within the constrained variable background range. The performance of the approach has been validated against 54 brain MR images, including images with severe intensity inhomogeneity and/or noise, CT chest images, and the Cameraman image. Compared with nonsupervised thresholding methods, the proposed approach is substantially more robust and more reliable. It is also computationally efficient and can be applied to a wide class of computer vision problems, such as character recognition, fingerprint identification, and segmentation of a wide variety of medical images.