Neuromorphic imaging reacts to per-pixel brightness changes of a dynamic scene with high temporal precision and responds with asynchronous streaming events as a result. It also often supports a simultaneous output of an intensity image. Nevertheless, the raw events typically involve a large amount of noise due to the high sensitivity of the sensor, while capturing fast-moving objects at low frame rates results in blurry images. These deficiencies significantly degrade human observation and machine processing. Fortunately, the two information sources are inherently complementary - events with microsecond-level temporal resolution, which are triggered by the edges of objects recorded in a latent sharp image, can supply rich motion details missing from the blurry one. In this work, we bring the two types of data together and introduce a simple yet effective unifying algorithm to jointly reconstruct blur-free images and noise-robust events in an iterative coarse-to-fine fashion. Specifically, an event-regularized prior offers precise high-frequency structures and dynamic features for blind deblurring, while image gradients serve as a kind of faithful supervision in regulating neuromorphic noise removal. Comprehensively evaluated on real and synthetic samples, such a synergy delivers superior reconstruction quality for both images with severe motion blur and raw event streams with a storm of noise, and also exhibits greater robustness to challenging realistic scenarios such as varying levels of illumination, contrast and motion magnitude. Meanwhile, it can be driven by much fewer events and holds a competitive edge at computational time overhead, rendering itself preferable as available computing resources are limited. Our solution gives impetus to the improvement of both sensing data and paves the way for highly accurate neuromorphic reasoning and analysis.

Inverse imaging covers a wide range of imaging applications, including super-resolution, deblurring, and compressive sensing. We propose a novel scheme to solve such problems by combining duality and the alternating direction method of multipliers (ADMM). In addition to a conventional ADMM process, we introduce a second one that solves the dual problem to find the estimated nontrivial lower bound of the objective function, and the related iteration results are used in turn to guide the primal iterations. We call this D-ADMM, and show that it converges to the global minimum when the regularization function is convex and the optimization problem has at least one optimizer. Furthermore, we show how the scheme can give rise to two specific algorithms, called D-ADMM-L2 and D-ADMM-TV, by having different regularization functions. We compare D-ADMM-TV with other methods on image super-resolution and demonstrate comparable or occasionally slightly better quality results. This paves the way of incorporating advanced operators and strategies designed for basic ADMM into the D-ADMM method as well to further improve the performances of those methods.