Deep Graph Laplacian Regularization for Robust Denoising of Real Images

Recent developments in deep learning have revolutionized the paradigm of image restoration. However, its applications on real image denoising are still limited, due to its sensitivity to training data and the complex nature of real image noise. In this work, we combine the robustness merit of model-based approaches and the learning power of data-driven approaches for real image denoising. Specifically, by integrating graph Laplacian regularization as a trainable module into a deep learning framework, we are less susceptible to overfitting than pure CNN-based approaches, achieving higher robustness to small datasets and cross-domain denoising. First, a sparse neighborhood graph is built from the output of a convolutional neural network (CNN). Then the image is restored by solving an unconstrained quadratic programming problem, using a corresponding graph Laplacian regularizer as a prior term. The proposed restoration pipeline is fully differentiable and hence can be end-to-end trained. Experimental results demonstrate that our work is less prone to overfitting given small training data. It is also endowed with strong cross-domain generalization power, outperforming the state-of-the-art approaches by a remarkable margin

Cascade Residual Learning: A Two-stage Convolutional Neural Network for Stereo Matching

Leveraging on the recent developments in convolutional neural networks (CNNs), matching dense correspondence from a stereo pair has been cast as a learning problem, with performance exceeding traditional approaches. However, it remains challenging to generate high-quality disparities for the inherently ill-posed regions. To tackle this problem, we propose a novel cascade CNN architecture composing of two stages. The first stage advances the recently proposed DispNet by equipping it with extra up-convolution modules, leading to disparity images with more details. The second stage explicitly rectifies the disparity initialized by the first stage; it couples with the first-stage and generates residual signals across multiple scales. The summation of the outputs from the two stages gives the final disparity. As opposed to directly learning the disparity at the second stage, we show that residual learning provides more effective refinement. Moreover, it also benefits the training of the overall cascade network. Experimentation shows that our cascade residual learning scheme provides state-of-the-art performance for matching stereo correspondence. By the time of the submission of this paper, our method ranks first in the KITTI 2015 stereo benchmark, surpassing the prior works by a noteworthy margin.Comment: Accepted at ICCVW 2017. The first two authors contributed equally to this pape

The Response Of The Equatorial Tropospheric Ozone To The Madden–Julian Oscillation In Tes Satellite Observations And Cam-Chem Model Simulation

The Madden-Julian Oscillation (MJO) is the dominant form of the atmospheric intra-seasonal oscillation, manifested by slow eastward movement (about 5 m/s) of tropical deep convection. This study investigates the MJO s impact on equatorial tropospheric ozone (10N-10S) in satellite observations and chemical transport model (CTM) simulations. For the satellite observations, we analyze the Tropospheric Emission Spectrometer (TES) level-2 ozone profile data for the period of Jan 2004 to Jun 2009. For the CTM simulations, we run the Community Atmosphere Model with chemistry (CAM-chem) driven by the GOES-5 analyzed meteorological fields for the same data period as the TES measurements. Our analysis indicates that the behavior of the Total Tropospheric Column (TTC) ozone at the intraseasonal time scale is different from that of the total column ozone, with the signal in the equatorial region comparable with that in the subtropics. The model simulated and satellite measured ozone anomalies agree in their general pattern and amplitude when examined in the vertical cross section (the average spatial correlation coefficient among the 8 phases is 0.63), with an eastward propagation signature at a similar phase speed as the convective anomalies (5 m/s). The model ozone anomalies on the intraseasonal time scale are about five times larger when lightning emissions of NOx are included in the simulation than when they are not. Nevertheless, large-scale advection is the primary driving force for the ozone anomalies associated with the MJO. The variability related to the MJO for ozone reaches up to 47% of the total variability (ranging from daily to interannual), indicating the MJO should be accounted for in simulating ozone perturbations in the tropics