This paper proposes a novel technique to create a high-resolution image by combining the bracketed exposure sequence without a priori knowledge of source image. The source image is split into three categories: constant, high varying and low varying feature images. For high and low varying features, pixels with highest information is selected and combined to construct collective high and low varying feature image. Collective constant feature image is constructed from weighted average of constant feature images, where weight is calculated based on information present in original source images. These pre-processed high, low and constant feature images are further combined to produce a final fused image. Objective analysis based quality evaluation parameters show a significant improvement in result produced by proposed method against the state-of-the-art.

Color images are widely used to disseminate information via websites and smartphone applications. Red-green deficients may have difficulty distinguishing the colors, and this would cause ineffective visual communication. Recently, a naturalness preserving image recoloring method for red-green deficients was introduced. However, the colors of objects in the recolored images may still look similar to them. Thus, this paper proposes a flexible color contrast enhancement method for red-green deficiency. The proposed method introduces a contrast parameter to exaggerate the blue stimulation of the recolored images. Moreover, the proposed method provides a flexible way for red-green deficients to view the image based on their preference. Objective and subjective evaluations were conducted to evaluate the performance of the proposed method. Results from subjective evaluation performed by the red-green deficients showed that the proposed method obtained better preference scores in terms of color contrast enhancement and their overall preference.

An encryption algorithm based on sparse coding and compressive sensing is proposed. Sparse coding is used to find the sparse representation of images as a linear combination of atoms from an overcomplete learned dictionary. The overcomplete dictionary is learned using K-SVD, utilizing non-overlapping patches obtained from a set of images. Compressed sensing is used to sample data at a rate below the Nyquist rate. A Gaussian measurement matrix compressively samples the plain image. As these measurements are linear, chaos based permutation and substitution operations are performed to obtain the cipher image. Bit-level scrambling and block substitution is done to confuse and diffuse the measurements. Simulation results verify the performance of the proposed technique against various statistical attacks.

This paper is concerned with the output feedback stabilization of two-dimensional discrete fuzzy systems described by the Fornasini–Marchesini second model. Based on the fuzzy-basis-dependent Lyapunov function, a new criterion is proposed for the fuzzy static output feedback (SOF) controller, which is expressed as strict linear matrix inequalities and hence numerically tractable. The main advantage of the developed SOF control scheme is that no constraints are imposed on system matrices, which is expected to have a wider range of applications. The applicability and the advantage of the proposed results are shown through two numerical examples.

After a careful review of the published version of our paper, we discovered that some references to the number of multiwavelet decomposition and reconstruction levels are incorrect.

The principal component analysis (PCA) is widely used for data decorrelation and dimensionality reduction. However, the use of PCA may be impractical in real-time applications, or in situations were energy and computing constraints are severe. In this context, the discrete cosine transform (DCT) becomes a low-cost alternative to data decorrelation. This paper presents a method to derive computationally efficient approximations to the DCT. The proposed method aims at the minimization of the angle between the rows of the exact DCT matrix and the rows of the approximated transformation matrix. The resulting transformations matrices are orthogonal and have extremely low arithmetic complexity. Considering popular performance measures, one of the proposed transformation matrices outperforms the best competitors in both matrix error and coding capabilities. Practical applications in image and video coding demonstrate the relevance of the proposed transformation. In fact, we show that the proposed approximate DCT can outperform the exact DCT for image encoding under certain compression ratios. The proposed transform and its direct competitors are also physically realized as digital prototype circuits using FPGA technology.

To perform the integration of radar and communication in waveform, we design an integrated system combining multiple-input multiple-output radar with orthogonal frequency division multiplexing (OFDM) communication. In this system, each antenna transmits the integrated waveform with a nonoverlapping block sub-frequency band. The utilized waveform is a variation of the classical OFDM communication waveform. In order to sufficiently exploit the entire system bandwidth and array aperture, a joint time and space processing approach is proposed, and hence the range and angle estimations with high resolution are obtained, whereas the range and angle are coupled. Moreover, the loss in processing gain and the Cramer–Rao bounds of range and angle estimates based on integrated waveform are derived, respectively. Theoretical analysis validates that the designed system is capable of implementing the radar and communication functions simultaneously. Finally, numerical results are presented to verify the effectiveness of the proposed approach.

An anti-deception jamming technique is proposed for moving target indication in a pulse-Doppler (PD) radar. The deceive targets are produced by digital radio frequency memory, which tries to pull off the range and velocity gates of real targets. Similar to orthogonal frequency division multiplexing, we use different sets of orthogonal sub-carriers in consecutive coherent pulse intervals (CPIs). By changing sub-carriers in different CPIs, we show that the deceive targets appear as interference in receiving signals. The generalized likelihood ratio test is used for detection and discrimination of real targets. The performance of the proposed method is achieved analytically and by simulations. Furthermore, we implement a hardware block using a TMS6416-DSK DSP for a PD radar prototype exploiting the proposed algorithm to deception discrimination. The presented results demonstrate the good accordance with theoretical predictions.

The problem of recovering a low-rank matrix from partial entries, known as low-rank matrix completion, has been extensively investigated in recent years. It can be viewed as a special case of the affine constrained rank minimization problem which is NP-hard in general and is computationally hard to solve in practice. One widely studied approach is to replace the matrix rank function by its nuclear-norm, which leads to the convex nuclear-norm minimization problem solved efficiently by many popular convex optimization algorithms. In this paper, we propose a new nonconvex approach to better approximate the rank function. The new approximation function is actually the Moreau envelope of the rank function (MER) which has an explicit expression. The new approximation problem of low-rank matrix completion based on MER can be converted to an optimization problem with two variables. We then adapt the proximal alternating minimization algorithm to solve it. The convergence (rate) of the proposed algorithm is proved and its accelerated version is also developed. Numerical experiments on completion of low-rank random matrices and standard image inpainting problems have shown that our algorithms have better performance than some state-of-art methods.

Nadimi et al. (Multidimens Syst Signal Process 2017. https://doi.org/10.1007/s11045-017-0519-3 ) studied the distribution of the ratio of two independent complex Gaussian random variables. The expressions provided for the distribution involved a hypergeometric function and an infinite sum. Here, we derive simpler and more manageable expressions. The practical usefulness of the expressions in terms of computational time is illustrated.