Real-time Controllable Denoising for Image and Video

Controllable image denoising aims to generate clean samples with human perceptual priors and balance sharpness and smoothness. In traditional filter-based denoising methods, this can be easily achieved by adjusting the filtering strength. However, for NN (Neural Network)-based models, adjusting the final denoising strength requires performing network inference each time, making it almost impossible for real-time user interaction. In this paper, we introduce Real-time Controllable Denoising (RCD), the first deep image and video denoising pipeline that provides a fully controllable user interface to edit arbitrary denoising levels in real-time with only one-time network inference. Unlike existing controllable denoising methods that require multiple denoisers and training stages, RCD replaces the last output layer (which usually outputs a single noise map) of an existing CNN-based model with a lightweight module that outputs multiple noise maps. We propose a novel Noise Decorrelation process to enforce the orthogonality of the noise feature maps, allowing arbitrary noise level control through noise map interpolation. This process is network-free and does not require network inference. Our experiments show that RCD can enable real-time editable image and video denoising for various existing heavy-weight models without sacrificing their original performance.Comment: CVPR 202

DiffusionMat: Alpha Matting as Sequential Refinement Learning

In this paper, we introduce DiffusionMat, a novel image matting framework that employs a diffusion model for the transition from coarse to refined alpha mattes. Diverging from conventional methods that utilize trimaps merely as loose guidance for alpha matte prediction, our approach treats image matting as a sequential refinement learning process. This process begins with the addition of noise to trimaps and iteratively denoises them using a pre-trained diffusion model, which incrementally guides the prediction towards a clean alpha matte. The key innovation of our framework is a correction module that adjusts the output at each denoising step, ensuring that the final result is consistent with the input image's structures. We also introduce the Alpha Reliability Propagation, a novel technique designed to maximize the utility of available guidance by selectively enhancing the trimap regions with confident alpha information, thus simplifying the correction task. To train the correction module, we devise specialized loss functions that target the accuracy of the alpha matte's edges and the consistency of its opaque and transparent regions. We evaluate our model across several image matting benchmarks, and the results indicate that DiffusionMat consistently outperforms existing methods. Project page at~\url{https://cnnlstm.github.io/DiffusionMa

Oxy-coal combustion in a 30kWth pressurized fluidized bed: Effect of combustion pressure on combustion performance, pollutant emissions and desulfurization

Oxy-coal combustion with pressurized fluidized beds has recently emerged as a promising carbon capture and storage (CCS) technology for coal-fired power plants. Although a large number of energy efficiency analyses have shown that an increase in combustion pressure can further increase the net plant efficiency, there are few experimental studies of pressurized oxy-coal combustion conducted on fluidized bed combustors/boilers with continuous coal feeding. In this study, oxy-coal combustion experiments with lignite and anthracite were conducted with a 30 kWth pressurized fluidized bed combustor within the pressure range of 0.1 MPa to 0.4 MPa. The investigation focused on the elucidation of the impacts of combustion pressure on the combustion performance, pollutant emissions and desulfurization of oxy-coal combustion in fluidized beds. The results showed that an increase in pressure increased the combustion efficiency and combustion rate of coal particles, and the promoting effect of pressure increase was more significant for the high rank coal with smaller particle size and the high O2 concentration atmosphere. For both coals, NOx emissions decreased with pressure but N2O emissions increased with pressure and accounted for a considerable part of the nitrogen oxide pollutants under high pressure oxy-coal combustion conditions. The pressure had insignificant impact on the SO2 emissions of oxy-coal combustion but an increase in pressure enhanced the direct desulfurization of limestone