相关论文: Resolving Microlens Blends Using Image Subtraction
The current modelling of single microlensing light curves neglects the possibility that only a fraction of the light is due to the lensed star, the remaining being due to a close, unresolved blend, which may be related or unrelated to the…
We reexamine the usefulness of fitting blended lightcurve models to microlensing photometric data. We find agreement with previous workers (e.g. Wozniak & Paczynski) that this is a difficult proposition because of the degeneracy of blend…
Microlensing can provide a useful tool to probe binary distributions down to low-mass limits of binary companions. In this paper, we analyze the light curves of 8 binary lensing events detected through the channel of high-magnification…
The large number of microlensing events discovered towards the Galactic Bulge bears the promise to reconstruct the stellar mass function. The more interesting issue concerning the mass function is certainly to probe its low mass end.…
Currently, many blind deblurring methods assume blurred images are noise-free and perform unsatisfactorily on the blurry images with noise. Unfortunately, noise is quite common in real scenes. A straightforward solution is to denoise images…
An extra-solar planet can be detected by microlensing because the planet can perturb the smooth lensing light curve created by the primary lens. However, it was shown by Gaudi that a subset of binary-source events can produce light curves…
Reproducing an all-in-focus image from an image with defocus regions is of practical value in many applications, eg, digital photography, and robotics. Using the output of some existing defocus map estimator, existing approaches first…
We present a new method designed for optimal subtraction of two images with different seeing. Using image subtraction appears to be essential for the full analysis of the microlensing survey images, however a perfect subtraction of two…
Real-world lighting often consists of multiple illuminants with different spectra. Separating and manipulating these illuminants in post-process is a challenging problem that requires either significant manual input or calibrated scene…
This paper proposes a non-computational method of counteracting the effect of image degradation introduced by the diffraction phenomenon in lensless microscopy. All the optical images (whether focused by lenses or not) are diffraction…
The biggest uncertainty in determining microlensing parameters comes from the blending of source star images because the current experiments are being carried out toward very dense star fields: the Galactic bulge and Magellanic Clouds. The…
Microlensing can provide an important tool to study binaries, especially those composed of faint or dark objects. However, accurate analysis of binary-lens light curves is often hampered by the well-known degeneracy between close (s<1) and…
We present a simple, yet effective diffusion-based method for fine-grained, parametric control over light sources in an image. Existing relighting methods either rely on multiple input views to perform inverse rendering at inference time,…
The goal of blind image deblurring is to recover a sharp image from a motion blurred one without knowing the camera motion. Current state-of-the-art methods have a remarkably good performance on images with no noise or very low noise…
If a light-emitting star is responsible for a gravitational microlensing event, the lens can be characterized by analyzing the blended light from the lens. In this paper, we investigate the feasibility of characterizing lenses by using this…
More than 40 years after the first discussion, it was recently reported the detection of a self-lensing phenomenon within a binary system where the brightness of a background star is magnified by its foreground companion. It is expected…
Low-light photography produces images with low signal-to-noise ratios due to limited photons. In such conditions, common approximations like the Gaussian noise model fall short, and many denoising techniques fail to remove noise…
Defocus deblurring is a challenging task due to the spatially varying nature of defocus blur. While deep learning approach shows great promise in solving image restoration problems, defocus deblurring demands accurate training data that…
Image decomposition is a crucial subject in the field of image processing. It can extract salient features from the source image. We propose a new image decomposition method based on convolutional neural network. This method can be applied…
The light curves of the OGLE microlensing candidates have been reconstructed using the image subtraction method. A large improvement of the photometric accuracy has been found in comparison with previous processing of the data with DoPHOT.…