SPS Feed

In this paper, we show that the adaptive projected subgradient method (APSM) is bounded perturbation resilient. To illustrate a potential application of this result, we propose a set-theoretic framework for MIMO detection, and we devise algorithms based on a superiorized APSM. Various low-complexity MIMO detection algorithms achieve excellent performance on i.i.d. Gaussian channels, but they typically incur high performance loss if realistic channel models (e.g., correlated channels) are considered.

This paper examines the problem of bipartite consensus for Takagi-Sugeno fuzzy multi-agent systems subject to uncertainties. The principal intention of this work is to develop a non-fragile controller through which the considered multi-agent system can achieve bipartite consensus. An undirected signed graph is considered to describe the cooperative and competitive interaction among neighboring agents.

In this paper, we investigate the performance of a wide area network (WAN) with three hops over a mixed radio frequency (RF), reconfigurable intelligent surface (RIS) assisted RF and Free space optics (FSO) channel. Here RIS and decode-and-forward (DF) relays are used to improve the coverage and system performance. For general applicability, the RF and FSO links are modelled with Saleh-Valenzuela (S-V) and Gamma-Gamma distribution, respectively.

We introduce graph wedgelets - a tool for data compression on graphs based on the representation of signals by piecewise constant functions on adaptively generated binary graph partitionings. The adaptivity of the partitionings, a key ingredient to obtain sparse representations of a graph signal, is realized in terms of recursive wedge splits adapted to the signal. For this, we transfer adaptive partitioning and compression techniques known for 2D images to general graph structures and develop discrete variants of continuous wedgelets and binary space partitionings.

Light Fields (LFs) are easily degraded by noise and low light. Low light LF enhancement and denoising are more challenging than single image tasks because the epipolar information among views should be taken into consideration. In this work, we propose a multiple stream progressive restoration network to restore the whole LF in just one forward pass. To make full use of the multiple views supplementary information and preserve the epipolar information, we design three types of input composed of view stacking.

Images captured in low-light environments suffer from serious degradation due to insufficient light, leading to the performance decline of industrial and civilian devices. To address the problems of noise, chromatic aberration, and detail distortion for enhancing low-light images using existing enhancement methods, this paper proposes an integrated learning approach (LightingNet) for low-light image enhancement. 

Robustness and stability of image-reconstruction algorithms have recently come under scrutiny. Their importance to medical imaging cannot be overstated. We review the known results for the topical variational regularization strategies ( ℓ2 and ℓ1 regularization) and present novel stability results for ℓp -regularized linear inverse problems for p∈(1,∞) . Our results guarantee Lipschitz continuity for small p and Hölder continuity for larger p . They generalize well to the Lp (Ω)  function spaces.

3D face reconstruction from a single image still suffers from low accuracy and inability to recover textures in invisible regions. In this paper, we propose a method for generating a 3D portrait with complete texture. The coarse face-and-head model and texture parameters are obtained using 3D Morphable Model fitting. We design an image-geometric inverse renderer that acquires normal, albedo, and light to jointly reconstruct the facial details.

The correlation filter(CF)-based tracker is a classic and effective model in the field of visual tracking. For a long time, most CF-based trackers solved filters using only ridge regression equations with l2 -norm, which can make the trained model noisy and not sparse. As a result, we propose a model of adaptive sparse spatially-regularized correlation filters (AS2RCF). Aiming to suppress the noise mixed in the model, we improve it by introducing an l1 -norm spatial regularization term.