Learning Deep CNN Denoiser Prior for Image Restoration

Model-based optimization methods and discriminative learning methods have been the two dominant strategies for solving various inverse problems in low-level vision. Typically, those two kinds of methods have their respective merits and drawbacks, e.g., model-based optimization methods are flexible for handling different inverse problems but are usually time-consuming with sophisticated priors for the purpose of good performance; in the meanwhile, discriminative learning methods have fast testing speed but their application range is greatly restricted by the specialized task. Recent works have revealed that, with the aid of variable splitting techniques, denoiser prior can be plugged in as a modular part of model-based optimization methods to solve other inverse problems (e.g., deblurring). Such an integration induces considerable advantage when the denoiser is obtained via discriminative learning. However, the study of integration with fast discriminative denoiser prior is still lacking. To this end, this paper aims to train a set of fast and effective CNN (convolutional neural network) denoisers and integrate them into model-based optimization method to solve other inverse problems. Experimental results demonstrate that the learned set of denoisers not only achieve promising Gaussian denoising results but also can be used as prior to deliver good performance for various low-level vision applications.Comment: Accepted to CVPR 2017. Code: https://github.com/cszn/ircn

Forecast B-modes detection at large scales in presence of noise and foregrounds

We investigate the detectability of the primordial CMB polarization B-mode power spectrum on large scales in the presence of instrumental noise and realistic foreground contamination. We have worked out a method to estimate the errors on component separation and to propagate them up to the power spectrum estimation. The performances of our method are illustrated by applying it to the instrumental specifications of the Planck satellite and to the proposed configuration for the next generation CMB polarization experiment COrE. We demonstrate that a proper component separation step is required in order achieve the detection of B-modes on large scales and that the final sensitivity to B-modes of a given experiment is determined by a delicate balance between noise level and residual foregrounds, which depend on the set of frequencies exploited in the CMB reconstruction, on the signal-to-noise of each frequency map, and on our ability to correctly model the spectral behavior of the foreground components. We have produced a flexible software tool that allows the comparison of performances on B-mode detection of different instrumental specifications (choice of frequencies, noise level at each frequency, etc.) as well as of different proposed approaches to component separation.Comment: 7 pages, 2 tables, 1 figure, accepted by MNRA

Towards dynamic camera calibration for constrained flexible mirror imaging

Flexible mirror imaging systems consisting of a perspective camera viewing a scene reflected in a flexible mirror can provide direct control over image field-of-view and resolution. However, calibration of such systems is difficult due to the vast range of possible mirror shapes and the flexible nature of the system. This paper proposes the fundamentals of a dynamic calibration approach for flexible mirror imaging systems by examining the constrained case of single dimensional flexing. The calibration process consists of an initial primary calibration stage followed by in-service dynamic calibration. Dynamic calibration uses a linear approximation to initialise a non-linear minimisation step, the result of which is the estimate of the mirror surface shape. The method is easier to implement than existing calibration methods for flexible mirror imagers, requiring only two images of a calibration grid for each dynamic calibration update. Experimental results with both simulated and real data are presented that demonstrate the capabilities of the proposed approach