Image Restoration using Total Variation Regularized Deep Image Prior

In the past decade, sparsity-driven regularization has led to significant improvements in image reconstruction. Traditional regularizers, such as total variation (TV), rely on analytical models of sparsity. However, increasingly the field is moving towards trainable models, inspired from deep learning. Deep image prior (DIP) is a recent regularization framework that uses a convolutional neural network (CNN) architecture without data-driven training. This paper extends the DIP framework by combining it with the traditional TV regularization. We show that the inclusion of TV leads to considerable performance gains when tested on several traditional restoration tasks such as image denoising and deblurring

DOLPH: Diffusion Models for Phase Retrieval

Phase retrieval refers to the problem of recovering an image from the magnitudes of its complex-valued linear measurements. Since the problem is ill-posed, the recovery requires prior knowledge on the unknown image. We present DOLPH as a new deep model-based architecture for phase retrieval that integrates an image prior specified using a diffusion model with a nonconvex data-fidelity term for phase retrieval. Diffusion models are a recent class of deep generative models that are relatively easy to train due to their implementation as image denoisers. DOLPH reconstructs high-quality solutions by alternating data-consistency updates with the sampling step of a diffusion model. Our numerical results show the robustness of DOLPH to noise and its ability to generate several candidate solutions given a set of measurements

Collider Interplay for Supersymmetry, Higgs and Dark Matter

We discuss the potential impacts on the CMSSM of future LHC runs and possible electron-positron and higher-energy proton-proton colliders, considering searches for supersymmetry via MET events, precision electroweak physics, Higgs measurements and dark matter searches. We validate and present estimates of the physics reach for exclusion or discovery of supersymmetry via MET searches at the LHC, which should cover the low-mass regions of the CMSSM parameter space favoured in a recent global analysis. As we illustrate with a low-mass benchmark point, a discovery would make possible accurate LHC measurements of sparticle masses using the MT2 variable, which could be combined with cross-section and other measurements to constrain the gluino, squark and stop masses and hence the soft supersymmetry-breaking parameters m_0, m_{1/2} and A_0 of the CMSSM. Slepton measurements at CLIC would enable m_0 and m_{1/2} to be determined with high precision. If supersymmetry is indeed discovered in the low-mass region, precision electroweak and Higgs measurements with a future circular electron-positron collider (FCC-ee, also known as TLEP) combined with LHC measurements would provide tests of the CMSSM at the loop level. If supersymmetry is not discovered at the LHC, is likely to lie somewhere along a focus-point, stop coannihilation strip or direct-channel A/H resonance funnel. We discuss the prospects for discovering supersymmetry along these strips at a future circular proton-proton collider such as FCC-hh. Illustrative benchmark points on these strips indicate that also in this case FCC-ee could provide tests of the CMSSM at the loop level.Comment: 47 pages, 26 figure

Characterization of transverse plasma jet and its effects on ramp induced separation

Plasma synthetic jet actuator (PSJA), which produces pulsed jets, is used to control the shock wave boundary layer interaction at a compression ramp at Ma = 2.0. The flow topology of the wall transverse plasma jet (TPJ) from the PSJA is first visualized through particle laser scattering (PLS) photography. The PSJA aperture effect is also examined by comparing the jets out of the apertures of 1.2 mm and 2 mm respectively. The control effect is later investigated by both PLS and particle image velocimetry (PIV). Further, the interaction process between the TPJ and the ramp-induced separation is discussed. The results show that the flow is characterized by two typical structures: the jet plume and the trailing vortex structures similar as those produced in the wake of vortex generator. And the PSJA with larger jet aperture is found to generate a stronger jet plume and the trailing vortices with a deeper penetration. Moreover, the reduced interaction region is only observed with the wider aperture through PLS technique. For PIV measurement, some further evidence on the reduced separated flow is given. The vortex shedding in the velocity shear layer is enhanced by the jet plume and the trailing vortex structures. Subsequently, the reduction of the separation zone is revealed with the overall shear layer reduced, which indicates the momentum exchange between the shear layer and mainstream. At last, a conceptual model based on two typical structures is suggested to reveal the control process

Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Condition

The plug-and-play priors (PnP) and regularization by denoising (RED) methods have become widely used for solving inverse problems by leveraging pre-trained deep denoisers as image priors. While the empirical imaging performance and the theoretical convergence properties of these algorithms have been widely investigated, their recovery properties have not previously been theoretically analyzed. We address this gap by showing how to establish theoretical recovery guarantees for PnP/RED by assuming that the solution of these methods lies near the fixed-points of a deep neural network. We also present numerical results comparing the recovery performance of PnP/RED in compressive sensing against that of recent compressive sensing algorithms based on generative models. Our numerical results suggest that PnP with a pre-trained artifact removal network provides significantly better results compared to the existing state-of-the-art methods.Comment: 27 pages, 13 figure