Drone Shadow Tracking

Aerial videos taken by a drone not too far above the surface may contain the drone's shadow projected on the scene. This deteriorates the aesthetic quality of videos. With the presence of other shadows, shadow removal cannot be directly applied, and the shadow of the drone must be tracked. Tracking a drone's shadow in a video is, however, challenging. The varying size, shape, change of orientation and drone altitude pose difficulties. The shadow can also easily disappear over dark areas. However, a shadow has specific properties that can be leveraged, besides its geometric shape. In this paper, we incorporate knowledge of the shadow's physical properties, in the form of shadow detection masks, into a correlation-based tracking algorithm. We capture a test set of aerial videos taken with different settings and compare our results to those of a state-of-the-art tracking algorithm.Comment: 5 pages, 4 figure

VIDIT: Virtual Image Dataset for Illumination Transfer

Deep image relighting is gaining more interest lately, as it allows photo enhancement through illumination-specific retouching without human effort. Aside from aesthetic enhancement and photo montage, image relighting is valuable for domain adaptation, whether to augment datasets for training or to normalize input test data. Accurate relighting is, however, very challenging for various reasons, such as the difficulty in removing and recasting shadows and the modeling of different surfaces. We present a novel dataset, the Virtual Image Dataset for Illumination Transfer (VIDIT), in an effort to create a reference evaluation benchmark and to push forward the development of illumination manipulation methods. Virtual datasets are not only an important step towards achieving real-image performance but have also proven capable of improving training even when real datasets are possible to acquire and available. VIDIT contains 300 virtual scenes used for training, where every scene is captured 40 times in total: from 8 equally-spaced azimuthal angles, each lit with 5 different illuminants.Comment: For further information and data, see https://github.com/majedelhelou/VIDI

Deep Gaussian Denoiser Epistemic Uncertainty and Decoupled Dual-Attention Fusion

Following the performance breakthrough of denoising networks, improvements have come chiefly through novel architecture designs and increased depth. While novel denoising networks were designed for real images coming from different distributions, or for specific applications, comparatively small improvement was achieved on Gaussian denoising. The denoising solutions suffer from epistemic uncertainty that can limit further advancements. This uncertainty is traditionally mitigated through different ensemble approaches. However, such ensembles are prohibitively costly with deep networks, which are already large in size. Our work focuses on pushing the performance limits of state-of-the-art methods on Gaussian denoising. We propose a model-agnostic approach for reducing epistemic uncertainty while using only a single pretrained network. We achieve this by tapping into the epistemic uncertainty through augmented and frequency-manipulated images to obtain denoised images with varying error. We propose an ensemble method with two decoupled attention paths, over the pixel domain and over that of our different manipulations, to learn the final fusion. Our results significantly improve over the state-of-the-art baselines and across varying noise levels.Comment: Code and models are publicly available on https://github.com/IVRL/DE

Closed-Form Solution to Disambiguate Defocus Blur in Single-Perspective Images

Depth-from-defocus techniques suffer an ambiguity problem where depth planes on opposite sides of the focal plane have identical defocus. We solve the ambiguity by relying on the wavelength-dependent relationship between defocus and depth. We conduct a robustness analysis and validation on consumer lenses

PoGaIN: Poisson-Gaussian Image Noise Modeling from Paired Samples

Image noise can often be accurately fitted to a Poisson-Gaussian distribution. However, estimating the distribution parameters from only a noisy image is a challenging task. Here, we study the case when paired noisy and noise-free samples are available. No method is currently available to exploit the noise-free information, which holds the promise of achieving more accurate estimates. To fill this gap, we derive a novel, cumulant-based, approach for Poisson-Gaussian noise modeling from paired image samples. We show its improved performance over different baselines with special emphasis on MSE, effect of outliers, image dependence and bias, and additionally derive the log-likelihood function for further insight and discuss real-world applicability.Comment: 5 pages, 4 figures, and 3 tables. Code is available at https://github.com/IVRL/PoGaI