Dual-Prior Integrated Image Reconstruction for Quanta Image Sensors Using Multi-Agent Consensus Equilibrium

Quanta image sensors(QIS) are a new type of singlephoton imaging device that can oversample the light field to generate binary bit-streams. The reconstruction for QIS refers to the recovery of original scenes from these binary measurements.Conventional reconstruction algorithms for QIS generally depend solely on one instantiated prior and are certainly insufficient for capturing the statistical properties over high-dimensional space.On the other hand, deep learning-based methods have shown promising performance, due to their excellent ability to learn feature representations from relevant databases. However, most deep models only focus on exploring local features while generally overlooking long-range similarity. In view of this, a dual-prior integrated reconstruction algorithm for QIS(DPI-QIS) is proposed, which combines a deep prior with a non-local self-similarity one using the multi-agent consensus equilibrium(MACE)framework. In comparison to the approaches that utilize a single prior, DPI-QIS fits the reconstruction model sufficiently by leveraging the respective merits of both priors. An effective yet flexible MACE framework is employed to integrate the physical forward model allying with the two prior-based models to achieve an overall better result. Extensive experiments demonstrate that the proposed algorithm achieves state-of-the-art performance in terms of objective and visual perception at multiple oversampling factors, while having stronger robustness to noise.

Catalytic ozonation in advanced treatment of kitchen wastewater:multi-scale simulation and pilot-scale study

Catalytic ozonation is regarded as a promising technology in the advanced treatment of refractory organic wastewater.Packed-bed reactors are widely used in practical applications due to simple structures,installation and operation.However,mass transfer of packed-bed reactors is relatively restrained and amplified deviations usually occurred in scale-up application.Herein,a multi-scale packed-bed model of catalytic ozonation was established to guide pilot tests.First,a laboratory-scale test was conducted to obtain kinetic parameters needed for modeling.Then,a multi-scale packed-bed model was developed to research the effects of water distribution structure,catalyst particle size,and hydraulic retention time(HRT)on catalytic ozonation.It was found that the performance of packed bed reactor was increased with evenly distributed water inlet,HRT of 60 min,and catalyst diameter of about 3-7 mm.Last,an optimized reactor was manufactured and a pilot-scale test was conducted to treat kitchen wastewater using catalytic ozonation process.In the pilot-scale test with an ozone dosage of 50 mg/L and HRT of 60 min,the packed-bed reactor filled with catalysts I was able to reduce chemical oxygen demand(COD)from 117 to 59 mg/L.The performance of the catalytic ozonation process in the packed-bed reactor for the advanced treatment of actual kitchen wastewater was investigated via both multi-scale simulation and pilot-scale tests in this study,which provided a practical method for optimizing the reactors of treating refractory organic wastewater.