Images captured under varying light conditions have deficient contrast, low brightness, latent colors, and high noise. Numerous methods have been developed for image enhancement. However, these methods are only suitable for enhancing specific type of images (e.g., over-exposed or underexposed), and also fail to restore artifact-free results for various other types of images. Therefore, to meet this goal, in this paper, we present an automatic image enhancement method, which is capable of producing quality results for all types of images captured under uneven exposure conditions (e.g., backlit, non-uniform, over-exposed, one-sided illumination and night-time images). Firstly, images are categorized using a convolutional neural network (CNN) to determine their class, and different values of weight coefficients are achieved for further processing. Then, images are converted into photonegative form to obtain an initial transmission map using a bright channel prior. Next, L1-norm regularization is adopted to refine scene transmission. Besides, environmental light is estimated based on an effective filter. Finally, the image degradation model is applied to achieve enhanced results. Furthermore, post-processing of the images is comprised of two steps, such as denoising and details enhancement. The denoised model is only applied when the images are captured in extreme low-light conditions. Whereas, a smooth layer is obtained using L1-norm regularization to enhance details in partially over-and under-exposed images. Extensive experiments reveal the effectives of the proposed approach as compared to other state-of-the-art algorithms.

Quadrature spatial modulation (QSM) based millimeter wave (mmWave) communication system design using frequency diverse array (FDA) is proposed in this article. Since spatial modulation techniques (SMT) employ maximum likelihood (ML) based detector at receiver, the computational complexity increases vastly. Moreover, numerous existing SMT methods utilize phased array (PA), where only angle dependent communication is possible. In this article, we propose to use a standard FDA that exploits a small linearly increasing frequency offset across the array for range-angle dependent QSM wireless communications. That is, the in-phase and quadrature components of transmission vector utilize slightly different frequencies to deliver the information. Furthermore, we propose a suboptimal multi-stage (MS) detector in the receiver, which applies a matched filtering (i.e., bandpass filters matched to the corresponding frequency offsets) approach to decode the index bits at first step, whereas, merely, two most probable estimated indices are utilized further to decode associated data bits using a standard ML method. Adhering an improved signal to noise ratio (SNR) due to an FDA based range-angle dependent transmission, simulation and numerical results show the improved performance of the proposed design over existing SM and QSM based schemes, while MS approach reduces the receiver's computational complexity.

Variational models involving Euler’s elastica energy have a wide range of applications in digital image processing. Recently fast methods such as proximal augmented Lagrangian method (PALM) have been successfully used to solve nonlinear higher order models for image restoration. In this paper, we extend fast method PALM to Euler’s elastica deconvolution models with quadratic and non-quadratic fidelity terms. The proposed variational model can eliminate blur, noise and preserve edges while reducing the blocky and staircase artifacts during image restoration. We present an efficient and effective solution to proposed minimization problems by a proximal-based numerical scheme. Our numerical experiments demonstrate several results on image deblurring and denoising, which shows a clear improvement of the proposed model over standard variational models such as total variation and Hessian-based model.