Single Image Noise Level Estimation Using Dark Channel Prior

Yeşilyurt, Aziz Berkay
Erol, Aybüke
Kamışlı, Fatih
Alatan, Abdullah Aydın
Noise level is required as an input parameter in various image processing applications. In this work, we use the dark channel prior (DCP) to estimate the noise level of an image degraded by additive white Gaussian noise. We develop an approximate model of the probability density function of the dark channel of the noisy image. Using this model, the noise level is determined with the maximum likelihood estimation method from the dark channel intensity values of the noisy image. The results show that our method is faster than the state-of-the-art methods by about two orders of magnitude while providing slightly inferior estimation performance.


Blind Communications Sytem Structures For SIMO Channels
Karakuetuek, Serkan; Tuncer, Temel Engin (2008-04-22)
Frame based single input multi output (SIMO) communications systems are considered. In these systems frame length, offset, channel order and channel coefficients are all unknowns and there is no training session. Different system structures are considered for the estimation these unknowns blindly and best system structure is found. In addition a new method for channel order estimation is proposed.
Correlation tracking based on wavelet domain information
Ipek, HL; Yilmaz, I; Yardimci, YC; Cetin, AE (2003-08-07)
Tracking moving objects in video can be carried out by correlating a template containing object pixels with pixels of the current frame. This approach may produce erroneous results under noise. We determine a set of significant pixels on the object by analyzing the wavelet transform of the template and correlate only these pixels with the current frame to determine the next position of the object. These significant pixels are easily trackable features of the image and increase the performance of the tracker.
Rotation Calibration of Rigid Spherical Microphone Arrays for Multi-perspective 6DoF Audio Recordings
Olgun, Orhun; Erdem, Ege; Hacıhabiboğlu, Hüseyin (2021-09-08)
The preferred approach for multi-perspective six-degrees-of-freedom (6DoF) audio involves using multiple rigid spherical microphone arrays (RSMA) that can capture higher-order Ambisonics. RSMAs are spherically symmetric and allow the calculation of the local decomposition of sound fields over spherical harmonic functions. When multiple such arrays are used, multiple scattering occurs that can be equalized via methods that rely on multipole expansions if the positions of the arrays are known and the coordina...
3D perceptual soundfield reconstruction via sound field extrapolation
Erdem, Eg; Hacıhabiboğlui Hüseyin.; Department of Multimedia Informatics (2020)
Perceptual sound field reconstruction (PSR) is a spatial audio recording and reproduction method based on the application of stereophonic panning laws in microphone array design. PSR allows rendering a perceptually veridical and stable auditory perspective in the horizontal plane of the listener, and involves recording using nearcoincident microphone arrays. This thesis extends the two dimensional PSR concept to three dimensions and allows reconstructing an arbitrary sound field based on measurements with a...
Maximum likelihood autoregressive model parameter estimation with noise corrupted independent snapshots
Çayır, Ömer; Candan, Çağatay (2021-09-01)
Maximum likelihood autoregressive (AR) model parameter estimation problem with independent snapshots observed under white Gaussian measurement noise is studied. In addition to the AR model parameters, the measurement noise variance is also included among the unknowns of the problem to develop a general solution covering several special cases such as the case of known noise variance, noise-free snapshots, the single snapshot operation etc. The presented solution is based on the expectation-maximization metho...
Citation Formats
A. B. Yeşilyurt, A. Erol, F. Kamışlı, and A. A. Alatan, “Single Image Noise Level Estimation Using Dark Channel Prior,” 2019, Accessed: 00, 2020. [Online]. Available: