Clipped noisy images: Heteroskedastic modeling and practical denoising
Signal Processing
A new color filter array with optimal sensing properties
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
On the application of structured sparse model selection to JPEG compressed images
CCIW'11 Proceedings of the Third international conference on Computational color imaging
Computer Graphics Forum
Statistical detection of LSB matching using hypothesis testing theory
IH'12 Proceedings of the 14th international conference on Information Hiding
Analysis of classification accuracy for pre-filtered multichannel remote sensing data
Expert Systems with Applications: An International Journal
Simplified noise model parameter estimation for signal-dependent noise
Signal Processing
A unified framework for multi-sensor HDR video reconstruction
Image Communication
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We present a simple and usable noise model for the raw-data of digital imaging sensors. This signal-dependent noise model, which gives the pointwise standard-deviation of the noise as a function of the expectation of the pixel raw-data output, is composed of a Poissonian part, modeling the photon sensing, and Gaussian part, for the remaining stationary disturbances in the output data. We further explicitly take into account the clipping of the data (over- and under-exposure), faithfully reproducing the nonlinear response of the sensor. We propose an algorithm for the fully automatic estimation of the model parameters given a single noisy image. Experiments with synthetic images and with real raw-data from various sensors prove the practical applicability of the method and the accuracy of the proposed model.