Underwater Image Dehazing via Unpaired Image-to-image Translation

Underwater imaging has long been focused on dehazing and color correction to address severe degradation in the water medium. In this paper, we propose a learning-based image restoration method that uses Generative Adversarial Networks (GAN). For network generality and learning flexibility, we constituted unpaired image translation frameworks into image restoration. The proposed method utilizes multiple cyclic consistency losses that capture image characteristics and details of underwater images. To prepare unpaired images of clean and degraded scenes, we collected images from Flickr and filtered out false images using image characteristics. For validation, we extensively evaluated the proposed network on simulated and real underwater hazy images. Also, we tested our method on conventional computer vision algorithms, such as the level of edges and feature matching results.

     

MXene/Fluoropolymer-Derived Laser-Carbonaceous All-Fibrous Nanohybrid Patch for Soft Wearable Bioelectronics

While state-of-the-art skin-adhering fibrous electrodes have distinct benefits in personal wearable bioelectronics, considerable challenges persist in the production of fibrous-based soft conductive biosensing nanomaterials and their integration into efficient multisensing platforms. Here, an electrochemical-electrophysiological multimodal biosensing patch based on MXene/fluoropolymer nanofiber-derived hierarchical porous TiO₂ nanocatalyst interconnected 3D fibrous carbon nanohybrid electrodes is reported. The nanohybrid electrode is produced via a one-step laser carbonaceous thermal oxidation, resulting in excellent elctroconductivity (sheet resistance = 15.6 Ω sq⁻¹), rich active edges for effective electron transmission, and abundant support for enzyme immobilization. The features are attributed to three synergistic effects: i) conductivity of the interior, unoxidized MXene layers, ii) quick heterogeneous electron transmission of the exterior TiO₂ nanoparticles, and iii) the porous disordered carbon's electron “bridge” effects. Based on the foregoing, the nanohybrid modified biosensing patch integrated into textile is demonstrated to be capable of simultaneously and precisely monitoring sweat glucose with pH adjustment (sensitivity of 77.12 µA mm ⁻¹ cm⁻² within physiological concentrations of 0.01–2 × 10⁻³ m ) and electrocardiogram signals (signal-to-noise ratio = 37.63 dB). This novel approach paves the way for controlled investigations of the nanohybrid, for several functionalization and design options, and for the mass manufacturing capabilities required in real-world applications.     

Highly Stretchable and Strain-Insensitive Liquid Metal based Elastic Kirigami Electrodes (LM-eKE) (Adv. Funct. Mater. 30/2023)

Kirigami, a traditional paper-cutting art, is a promising method for creating mechanically robust circuitry for unconventional devices capable of extreme stretchability through structural deformation. In this study, this design approach is expanded upon by introducing Liquid Metal based Elastic Kirigami Electrodes (LM-eKE) in which kirigami-patterned soft elastomers are coated with eutectic gallium-indium (EGaIn) alloy. Overcoming the mechanical and electrical limitations of previous efforts with paper-like kirigami, the all soft LM-eKE can be stretched to 820% strain while the electrical resistance only increases by 33%. This is enabled by the fluidic properties of the EGaIn coating, which maintains high electrical conductivity even as the elastic substrate undergoes extreme deformation. Applying the LM-eKE to human knee joints and fingers, the resistance change during physical activities is under 1.7%, thereby allowing for stable electrical operation of wearable health monitoring devices for tracking electroencephalogram (EEG) signals and other physiological activity.