Modeling the space of camera response functions

By M. Grossberg, Shree K. Nayar. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004

324 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/770d6de87513dd91e79839df361dfdc425297a8b

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In Atomic and Molecular Physics, and Optics

In Computer Vision and Pattern Recognition

Citations in the corpus

Attention Guided Low-Light Image Enhancement with a Large Scale Low-Light Simulation Dataset (2019, methodology): “In our experiments, f (·) are uniformly sampled from 201 CRFs provided in Grossberg and Nayar (2004) and M(·) represents the demosaicing function proposed in Malvar et al. (2004). We do not consider compression in this paper.”
Modeling inter-camera space-time and appearance relationships for tracking across non-overlapping views (2008, background): “This issue is discussed in the next two sections.”
A Holistic Approach to Cross-Channel Image Noise Modeling and Its Application to Image Denoising (2016, unknown): “Using the space of camera response functions [9], they used a Bayesian MAP estimation to infer the NLF from a single image.”
BLIND CONTRAST ENHANCEMENT ASSESSMENT BY GRADIENT RATIOING AT VISIBLE EDGES (2011, methodology): “Instead, following the approach described in (Hautiere and Dumont, 2007), it is proposed to compute, for each pixel belonging to a visible edge in the restored image, the following coefficient r: r = f 1(DIr)/f 1(DIo) (5) where DIr denotes the gradient in the restored image, DIo the gradient in the ”
What is the space of spectral sensitivity functions for digital color cameras? (2013, background): “The recovery accuracy is about 0.06, almost invariant to daylight at different time of the day.”
Noise Estimation from a Single Image (2006, methodology): “Since it may not be possible to find image regions at all the desired levels of intensity or color needed to describe the noise level function, we use the Columbia camera response function database [6] to model the NLF using principal components and bounds on derivatives.”
Optical flow modeling and computation: A survey (2015, background): “The more general approach of [74] parametrizes intensity changes in terms of Brightness Transfer Function [106], which coefﬁcients are learned from training data.”