Non-blind Algorithm Research Articles

Deblurring Turbulent Images via Maximizing L1 Regularization

Atmospheric turbulence significantly degrades image quality. A blind image deblurring algorithm is needed, and a favorable image prior is the key to solving this problem. However, the general sparse priors support blurry images instead of explicit images, so the details of the restored images are lost. The recently developed priors are non-convex, resulting in complex and heuristic optimization. To handle these problems, we first propose a convex image prior; namely, maximizing L1 regularization (ML1). Benefiting from the symmetrybetween ML1 and L1 regularization, the ML1 supports clear images and preserves the image edges better. Then, a novel soft suppression strategy is designed for the deblurring algorithm to inhibit artifacts. A coarse-to-fine scheme and a non-blind algorithm are also constructed. For qualitative comparison, a turbulent blur dataset is built. Experiments on this dataset and real images demonstrate that the proposed method is superior to other state-of-the-art methods in blindly recovering turbulent images.

Blind Image Forgery Detection by using DCT and SURF Based Algorithm

A remarkable array of visual images surrounds us. Image forgery by using digital technology has eroded confidence in the integrity of imagery. Image forensics is an aid to re-establish the trust and originality of imagery. It is important to identify the type of forgery for application of relevant image forensic technique for optimal output. Based on the availability of the original image, image forensic algorithms are classified in the blind and non-blind algorithms. But the availability of original imagery is not always necessary for all the cases of image tampering. The blind forgery detection is particularly difficult. Key point scheme is introduced instead of block based Discrete Cosine Transformation (DCT) and after the validation, the state of the art blind image forgery techniques, i.e., DCT and proposed Speeded-Up Robust-Features (SURF) algorithms are evaluated. Average Accuracy, True Positive Rate (TPR), and True Negative Rate (TNR) are used as parameters for performance evaluation on various images. Experimental results concluded the efficiency of SURF scheme in dealing with region rotation manipulations. The paper concluded with the efficiency of the key-point based scheme.

A review: algorithm used for beam forming systems

Wireless communication uses a smart antenna to provide better coverage and capacity for the communication system. Main functions performed by the smart antenna are Direction of Arrival estimation (DOA) and beamforming (DBF). The beam forming is signal processing techniques which combine antenna array technology with high-performance up/down-conversion, analog to digital conversion and digital signal processing to provide receivers with very high spatial selectivity. This paper evaluates non-blind algorithm such as LMS, to compute the weight calculation for phased array antenna using Matlab Simulink.

Single image super-resolution via blind blurring estimation and anchored space mapping

It has been widely acknowledged that learning-based super-resolution (SR) methods are effective to recover a high resolution (HR) image from a single low resolution (LR) input image. However, there exist two main challenges in learning-based SR methods currently: the quality of training samples and the demand for computation. We proposed a novel framework for single image SR tasks aiming at these issues, which consists of blind blurring kernel estimation (BKE) and SR recovery with anchored space mapping (ASM). BKE is realized via minimizing the cross-scale dissimilarity of the image iteratively, and SR recovery with ASM is performed based on iterative least square dictionary learning algorithm (ILS-DLA). BKE is capable of improving the compatibility of training samples and testing samples effectively and ASM can reduce consumed time during SR recovery radically. Moreover, a selective patch processing (SPP) strategy measured by average gradient amplitude |grad| of a patch is adopted to accelerate the BKE process. The experimental results show that our method outruns several typical blind and non-blind algorithms on equal conditions.

Quality-aware features-based noise level estimator for block matching and three-dimensional filtering algorithm

The performance of conventional denoising algorithms is usually controlled by one or several parameters whose optimal settings depend on the contents of the processed images and the characteristics of the noises. Among these parameters, noise level is a fundamental parameter that is always assumed to be known by most of the existing denoising algorithms (so-called nonblind denoising algorithms), which largely limits the applicability of these nonblind denoising algorithms in many applications. Moreover, these nonblind algorithms do not always achieve the best denoised images in visual quality even when fed with the actual noise level parameter. To address these shortcomings, in this paper we propose a new quality-aware features-based noise level estimator (NLE), which consists of quality-aware features extraction and optimal noise level parameter prediction. First, considering that image local contrast features convey important structural information that is closely related to image perceptual quality, we utilize the marginal statistics of two local contrast operators, i.e., the gradient magnitude and the Laplacian of Gaussian (LOG), to extract quality-aware features. The proposed quality-aware features have very low computational complexity, making them well suited for time-constrained applications. Then we propose a learning-based framework where the noise level parameter is estimated based on the quality-aware features. Based on the proposed NLE, we develop a blind block matching and three-dimensional filtering (BBM3D) denoising algorithm which is capable of effectively removing additive white Gaussian noise, even coupled with impulse noise. The noise level parameter of the BBM3D algorithm is automatically tuned according to the quality-aware features, guaranteeing the best performance. As such, the classical block matching and three-dimensional algorithm can be transformed into a blind one in an unsupervised manner. Experimental results demonstrate that the performance of the BBM3D algorithm is superior to its nonblind version both in visual qualitative evaluations and quantitative comparisons for various images and noise levels.

Visual Attention-Based Image Watermarking

Imperceptibility and robustness are two complementary but fundamental requirements of any watermarking algorithm. Low strength watermarking yields high imperceptibility but exhibits poor robustness. High strength watermarking schemes achieve good robustness but often infuse distortions resulting in poor visual quality in host media. If distortion due to high strength watermarking can avoid visually attentive regions, such distortions are unlikely to be noticeable to any viewer. In this paper, we exploit this concept and propose a novel visual attention-based highly robust image watermarking methodology by embedding lower and higher strength watermarks in visually salient and non-salient regions, respectively. A new low complexity wavelet domain visual attention model is proposed that allows us to design new robust watermarking algorithms. The proposed new saliency model outperforms the state-of-the-art method in joint saliency detection and low computational complexity performances. In evaluating watermarking performances, the proposed blind and non-blind algorithms exhibit increased robustness to various natural image processing and filtering attacks with minimal or no effect on image quality, as verified by both subjective and objective visual quality evaluation. Up to 25% and 40% improvement against JPEG2000 compression and common filtering attacks, respectively, are reported against the existing algorithms that do not use a visual attention model.

Variational semi-blind sparse deconvolution with orthogonal kernel bases and its application to MRFM

We present a variational Bayesian method of joint image reconstruction and point spread function (PSF) estimation when the PSF of the imaging device is only partially known. To solve this semi-blind deconvolution problem, prior distributions are specified for the PSF and the 3D image. Joint image reconstruction and PSF estimation is then performed within a Bayesian framework, using a variational algorithm to estimate the posterior distribution. The image prior distribution imposes an explicit atomic measure that corresponds to image sparsity. Importantly, the proposed Bayesian deconvolution algorithm does not require hand tuning. Simulation results clearly demonstrate that the semi-blind deconvolution algorithm compares favorably with previous Markov chain Monte Carlo (MCMC) version of myopic sparse reconstruction. It significantly outperforms mismatched non-blind algorithms that rely on the assumption of the perfect knowledge of the PSF. The algorithm is illustrated on real data from magnetic resonance force microscopy (MRFM).

CMA an Optimum Beamformer for a Smart Antenna System

paper constant modulus algorithm (CMA) and least mean square (LMS), kind of blind and nonblind algorithms used for adaptive beamforming are presented. These algorithms are embedded in smart antenna which calculates optimum weight vector that minimizes the total received power except the power coming from desired direction. The efficiency of CMA and LMS algorithms is compared on the basis of gain versus angle and mean square error (MSE) for mobile communication. Simulation results reveal that both algorithms have high resolution for beam formation. However CMA has good performance to minimize MSE as compared to LMS. Therefore, CMA is found more efficient algorithm to implement in the mobile communication environment to enhance service quality and capacity.

Steady-state analysis of constrained normalized adaptive filters for MAI reduction by energy conservation arguments

We derive the steady-state performance of a common class of adaptive filters for multiple access interference (MAI) reduction in code division multiple access (CDMA) systems. The adaptive filters under study utilize estimates of the desired user's amplitude and are divided into three groups of least-mean-square (LMS) algorithms differing by the choice of the normalization factor. The steady-state performance is deduced from energy-conservation relations that include a possibly erroneous estimate of the desired user's amplitude. The analyses show that blind algorithms using information about the desired user's amplitude achieve similar performance to that of nonblind algorithms. In addition, geometric considerations reveal the conditions under which the choice of the normalization factor is expected to have great impact on the convergence properties of the algorithms. Numerical simulations show good agreement with theory.

Double-filtering receivers for PSK signals transmitted over Rayleigh frequency-flat fading channels

This paper describes novel detection algorithms for coded and uncoded M-ary phase shift keying (M-PSK) signals transmitted over frequency-flat fading channels. Fading distortion is estimated with per-survivor methods using the sampled outputs of two receive filters. Some of the proposed algorithms do not require information on the fading statistics and are dubbed blind algorithms. The error rate performance of blind and nonblind algorithms is assessed by simulation for uncoded and trellis-coded phase shift keying (PSK) and is compared to the performance of other detection schemes proposed in the literature.