This paper presents an image enhancement model, D2BGAN (Dark to Bright Generative Adversarial Network), to translate low light images to bright images without a paired supervision. We introduce the use of geometric and lighting consistency along with a contextual loss criterion. These when combined with multiscale color, texture and edge discriminators prove to provide competitive results. We performed extensive experiments using benchmark datasets to visually and objectively compare our results. We observed the performance of D2BGAN on real-time driving datasets that are subject to motion blur, noise, and other artifacts. We further demonstrated that our enhanced images can be profitably used in image-understanding tasks. Images processed using our technique obtain the best or second best average scores for three different image quality evaluation methods on the Naturalness Preserved Enhancement (NPE), Low Light Image Enhancement (LIME), Multi-Exposure Image Fusion (MEF) benchmark datasets. Best scores are also obtained on the LOw-Light (LOL) test set and on Berkeley Driving Dataset (BDD) images processed with D2BGAN. Face detection tasks on the DarkFace benchmark dataset show an mAP (mean Average Precision) improvement from 0.209 to 0.301 when images are processed using D2BGAN. mAP further improves to 0.525 when finetuning techniques are adopted.

The powerful resource of parallelizing simple devices for realizing and enhancing complex operations comes with the drawback of multiple connections for addressing and controlling the individual elements. Here we report on a technological platform where several mechanical resonators can be individually probed and electrically actuated by using dispersive multiplexing within a single electrical channel. We demonstrate room temperature control of the individual device vibrational motion and spatially-resolved readouts. As the single elements have proven to be excellent bolometers and individual nodes for reservoir computing, our platform can be directly employed for single-channel addressing of multiple devices, with immediate applications for far-infrared cameras, spatial light modulators and recurrent neural networks operating at room temperature.

Field programmable gate arrays (FPGAs) have not only enhanced traditional sensing methods, such as pixel detection (CCD and CMOS), but also enabled the development of innovative approaches with significant potential for particle detection. This is particularly relevant in terahertz (THz) ray detection, where microbolometer-based focal plane arrays (FPAs) using microelectromechanical (MEMS) resonators are among the most promising solutions. Designing high-performance, high-pixel-density sensors is challenging without FPGAs, which are crucial for deterministic parallel processing, fast ADC/DAC control, and handling large data throughput. This paper presents a MEMS-resonator detector, fully managed via an FPGA, capable of controlling pixel excitation and tracking resonance-frequency shifts due to radiation using parallel digital lock-in amplifiers. The innovative FPGA architecture, based on a lock-in matrix, enhances the open-loop readout technique by a factor of 32. Measurements were performed on a frequency-multiplexed, 256-pixel sensor designed for imaging applications.

Atomic-scale imaging using scanning probe microscopy is a pivotal method for investigating the morphology and physico-chemical properties of nanostructured surfaces. Time resolution represents a significant limitation of this technique, as typical image acquisition times are on the order of several seconds or even a few minutes, while dynamic processes—such as surface restructuring or particle sintering, to be observed upon external stimuli such as changes in gas atmosphere or electrochemical potential—often occur within timescales shorter than a second. In this article, we present a fully redesigned field programmable gate array (FPGA)-based instrument that can be integrated into most commercially available standard scanning probe microscopes. This instrument not only significantly accelerates the acquisition of atomic-scale images by orders of magnitude but also enables the tracking of moving features such as adatoms, vacancies, or clusters across the surface (“atom tracking”) due to the parallel execution of sophisticated control and acquisition algorithms and the fast exchange of data with an external processor. Each of these measurement modes requires a complex series of operations within the FPGA that are explained in detail.