NoiSER: Noise is All You Need for Low-Light Image Enhancement

In this paper, we present an embarrassingly simple yet effective solution to a seemingly impossible mission, low-light image enhancement (LLIE) without access to any task-related data. The proposed solution, Noise SElf-Regression (NoiSER), simply learns a convolutional neural network equipped with a instance-normalization layer by taking a random noise image, $\mathcal{N}(0,\sigma^2)$ for each pixel, as both input and output for each training pair, and then the low-light image is fed to the learned network for predicting the normal-light image. Technically, an intuitive explanation for its effectiveness is as follows: 1) the self-regression reconstructs the contrast between adjacent pixels of the input image, 2) the instance-normalization layers may naturally remediate the overall magnitude/lighting of the input image, and 3) the $\mathcal{N}(0,\sigma^2)$ assumption for each pixel enforces the output image to follow the well-known gray-world hypothesis \cite{Gary-world_Hypothesis} when the image size is big enough, namely, the averages of three RGB components of an image converge to the same value. Compared to existing SOTA LLIE methods with access to different task-related data, NoiSER is surprisingly highly competitive in enhancement quality, yet with a much smaller model size, and much lower training and inference cost. With only $\sim$ 1K parameters, NoiSER realizes about 1 minute for training and 1.2 ms for inference with 600x400 resolution on RTX 2080 Ti. As a bonus, NoiSER possesses automated over-exposure suppression ability and shows excellent performance on over-exposed photos

FCL-GAN: A Lightweight and Real-Time Baseline for Unsupervised Blind Image Deblurring

Blind image deblurring (BID) remains a challenging and significant task. Benefiting from the strong fitting ability of deep learning, paired data-driven supervised BID method has obtained great progress. However, paired data are usually synthesized by hand, and the realistic blurs are more complex than synthetic ones, which makes the supervised methods inept at modeling realistic blurs and hinders their real-world applications. As such, unsupervised deep BID method without paired data offers certain advantages, but current methods still suffer from some drawbacks, e.g., bulky model size, long inference time, and strict image resolution and domain requirements. In this paper, we propose a lightweight and real-time unsupervised BID baseline, termed Frequency-domain Contrastive Loss Constrained Lightweight CycleGAN (shortly, FCL-GAN), with attractive properties, i.e., no image domain limitation, no image resolution limitation, 25x lighter than SOTA, and 5x faster than SOTA. To guarantee the lightweight property and performance superiority, two new collaboration units called lightweight domain conversion unit(LDCU) and parameter-free frequency-domain contrastive unit(PFCU) are designed. LDCU mainly implements inter-domain conversion in lightweight manner. PFCU further explores the similarity measure, external difference and internal connection between the blurred domain and sharp domain images in frequency domain, without involving extra parameters. Extensive experiments on several image datasets demonstrate the effectiveness of our FCL-GAN in terms of performance, model size and reference time

Cost/Benefit Analysis of Fuel-Efficient Speed Control Using Signal Phasing and Timing (SPaT) Data: Evaluation for Future Connected Corridor Deployment

(c) 1036220The objective of this methodology is to refine the preliminary results from previous work (11% fuel savings for one vehicle, one intersection) to an entire corridor of SPaT signals, with different CV market penetration, and with driver awareness of fuel savings benefits. The research will proceed in three parts. First, several vehicles will be instrumented with DSRC receivers and GPS tracking to record SPaT data and the vehicle trajectories together. Offline, the project team will optimize the speed and powertrain control based on recorded SPaT data, using the recorded vehicle trajectories to identify the constraints of traffic flow. A living lab consisting of a GM car engine loaded by a transient hydrostatic dynamometer will be used to measure the fuel consumption with and without speed control. Second, the project team will conduct traffic flow simulations to study the impacts of higher market penetration on the overall fuel benefits, including the benefits to legacy vehicles which unintentionally use SPaT based speed controls by following CVs. Third, network models will be used to predict changes in route choices as drivers recognize the benefits of fuel savings in the route utility. The numerical predictions of fuel savings will be combined into cost/benefit analyses to inform MnDOT on the future deployment of SPaT on other corridors

Hydrothermal Conversion of Red Mud into Magnetic Adsorbent for Effective Adsorption of Zn(II) in Water

Red mud, a Fe-rich waste generated from the aluminum industry, was recovered as an adsorbent for wastewater treatment. The separation process of red mud from water after adsorption, including centrifugation and filtration, was complicated. This study demonstrated an alternative option to recycle red mud for preparing magnetic adsorbent via a facile hydrothermal route using ascorbic acid as reductant. Red mud is weakly magnetized and consists of andradite, muscovite, hematite, and cancrinite. After hydrothermal treatment, andradite in red mud was reductively dissolved by ascorbic acid, and transformed into magnetite and morimotoite. With increasing hydrothermal temperature, the dissolution of andradite accelerated, and the crystallite size of magnetite increased. When the hydrothermal temperature reached 200 °C, the prepared adsorbent P-200 showed a desirable saturation magnetization of 4.1 Am2/kg, and could be easily magnetically separated from water after adsorption. The maximum adsorption capacity of P-200 for Zn2+ was 89.6 mg/g, which is eight-fold higher than that of the raw red mud. The adsorption of Zn2+ by P-200 fitted the Langmuir model, where cation exchange was the main adsorption mechanism. The average distribution coefficient of Zn2+ at low ppm level was 16.81 L/g for P-200, higher than those of the red mud (0.3 L/g) and the prepared P-120 (1.48 L/g) and P-270 (5.48 L/g), demonstrating that P-200 had the best adsorption capacity for Zn2+ and can be served as a practical adsorbent for real-world applications. To our knowledge, this is the first study to report the conversion of red mud into a magnetic adsorbent under mild conditions