Refining raw pixel values using a value error model to drive texture synthesis

Research output: Contribution to journalConference articlepeer-review


The goal in photography is generally the construction of a model of scene appearance. Unfortunately, statistical variations introduced by photon shot and other noise introduce errors in the raw value reported for each pixel sample. Rather than simply accepting those values as the best raw representation, the current work treats them as initial estimates of the correct values, and computes an error model for each pixel's value. The value error models for all pixels in an image are then used to drive a type of texture synthesis which refines the pixel value estimates, potentially increasing both accuracy and precision of each value. Each refined raw pixel value is synthesized from the value estimates of a plurality of pixels with overlapping error bounds and similar context within the same image. The error modeling and texture synthesis algorithms are implemented in and evaluated using KREMY (Kentucky Raw Error Modeler, pronounced "creamy"), a free software tool created for this purpose.

Original languageEnglish
Article numbers10
Pages (from-to)56-66
Number of pages11
JournalIS and T International Symposium on Electronic Imaging Science and Technology
StatePublished - 2017
EventImage Processing: Algorithms and Systems XV, IPAS 2017 - Burlingame, United States
Duration: Jan 29 2017Feb 2 2017

Bibliographical note

Funding Information:
This work is supported in part under NSF Award #1422811, CSR: Small: Computational Support for Time Domain Continuous Imaging.

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Human-Computer Interaction
  • Software
  • Electrical and Electronic Engineering
  • Atomic and Molecular Physics, and Optics


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