• Corpus ID: 118470235

Image Subtraction Noise Reduction Using Point Spread Function Cross-correlation

  title={Image Subtraction Noise Reduction Using Point Spread Function Cross-correlation},
  author={Steven Hartung},
  journal={arXiv: Instrumentation and Methods for Astrophysics},
  • Steven Hartung
  • Published 8 January 2013
  • Physics
  • arXiv: Instrumentation and Methods for Astrophysics
Image subtraction in astronomy is a tool for transient object discovery and characterization, particularly useful in wide fields, and is well suited for moving or photometrically varying objects such as asteroids, extra-solar planets and supernovae. A convolution technique is used to match point spread functions (PSFs) between images of the same field taken at different times prior to pixel-by-pixel subtraction. Particularly suitable for large-scale images is a spatially-varying kernel, where… 

Figures from this paper

Example-Based Face-Image Restoration for Block-Noise Reduction

Two methods to restore degraded face images based on an example-based Super-Resolution (SR) method are proposed, each of which results in better quality for highly-compressed images compared to the conventional Gaussian-filtering method and Smooth method.



A Method for Optimal Image Subtraction

We present a new method designed for optimal subtraction of two images with different seeing. Using image subtraction appears to be essential for full analysis of microlensing survey images; however,

Optimal Image Subtraction Method: Summary Derivations, Applications, and Publicly Shared Application Using IDL

Computer algorithms for the OIS method were developed, written using the Interactive Data Language (IDL) and applications demonstrating these algorithms are presented, and a complete description of the Gaussian components basis vectors used by Alard & Lupton to construct the convolution kernel is presented.

Image subtraction using a space-varying kernel

Image subtraction is a method by which one image is matched against another by using a convolution kernel, so that they can be differenced to detect and measure variable objects. It has been

Regularization techniques for PSF-matching kernels - I. Choice of kernel basis

A new technique is introduced, which bridges the gap between the generality of delta-function kernels and the compactness of sum-of-Gaussian kernels, to create general kernel solutions that represent the intrinsic shape of the PSF-matching kernel with only one degree of freedom, the strength of the regularization λ.

Signals and systems

A report on the 2006 Keystone Conference on Signaling Networks, Vancouver, Canada, 30 January-4 February 2006.