The camera calibration parameters and the image processing chain which generated a given image are generally not available to the receiver. This happens for example with scanned photographs and for most JPEG images. These images have undergone various nonlinear contrast changes and also linear and nonlinear filters. To deal with remnant noise in such images, we introduce a general nonparametric intensity and frequency-dependent noise model. We demonstrate by simulated and experiments with real images that this model, which requires the estimation of more than 1000 parameters, performs an efficient noise estimation. The proposed noise model is a patch model. Its estimation can therefore be used as a preliminary step to any patch-based denoising method. Our noise estimation method introduces several new tools for performing this complex estimation. One of them is a new sparse patch distance function permitting to find noisy patches with similar underlying geometry. A validation of the noise model and of its estimation method is obtained by comparing its results to ground-truth noise curves for both raw and JPEG-encoded images, and by visual inspection of the denoising results of real images. A fair comparison with the state of the art is also performed.

Arguably several thousands papers are dedicated to image denoising. Most papers assume a fixed noise model, mainly white Gaussian or Poissonian. This assumption is only valid for raw images. Yet, in most images handled by the public and even by scientists, the noise model is imperfectly known or unknown. End users only dispose the result of a complex image processing chain effectuated by uncontrolled hardware and software (and sometimes by chemical means). For such images, recent progress in noise estimation permits to estimate from a single image a noise model, which is simultaneously signal and frequency dependent. We propose here a multiscale denoising algorithm adapted to this broad noise model. This leads to a blind denoising algorithm which we demonstrate on real JPEG images and on scans of old photographs for which the formation model is unknown. The consistency of this algorithm is also verified on simulated distorted images. This algorithm is finally compared with the unique state of the art previous blind denoising method.

We present a non-parametric method estimating an intensity and frequency dependent noise from a single image. The noise model is estimated on image patches and can be used consequently in all patch-based denoising methods. The method applies to cases where no access is granted to the image noise model, in particular to scanned photographs and JPEG images. The general noise model and the method to evaluate it are validated by comparing the estimations with the corresponding ground-truth curves for raw and JPEG images. Denoising experiments on scanned photographs also support the efficiency of the estimation method.

Most papers on denoising methods assume a white Gaussian noise model. Yet in most images handled by the public or by scientific users, the noise model is unknown and is not white, because of the various processes applied to the image before it reaches the user: scanning, demosaicing, compression, de-convolution, etc. To cope with this wide ranging problem, we propose a blind multiscale denoising algorithm working for noise which is simultaneously signal and frequency dependent. On noisy images coming from diverse sources (JPEG, scans of old photographs, ...) we show perceptually convincing results. This algorithm is compared to the state-of-the-art and it is also validated on images with white noise.

The current state-of-the-art non-local algorithms for image denoising have the tendency to remove many low contrast details. Frequency-based algorithms keep these details, but on the other hand many artifacts are introduced. Recently, the Dual Domain Image Denoising (DDID) method has been proposed to address this issue. While beating the state-of-the-art, this algorithm still causes strong frequency domain artifacts. This paper reviews DDID under a different light, allowing to understand their origin. The analysis leads to the development of NLDD, a new denoising algorithm that outperforms DDID, BM3D and other state-of-the-art algorithms. NLDD is also three times faster than DDID and easily parallelizable.

Recent state-of-the-art image denoising methods use nonparametric estimation processes for $8 \times 8$ patches and obtain surprisingly good denoising results. The mathematical and experimental evidence of two recent articles suggests that we might even be close to the best attainable performance in image denoising ever. This suspicion is supported by a remarkable convergence of all analyzed methods. Still more interestingly, most patch-based image denoising methods can be summarized in one paradigm, which unites the transform thresholding method and a Markovian Bayesian estimation. As the present paper shows, this unification is complete when the patch space is assumed to be a Gaussian mixture. Each Gaussian distribution is associated with its orthonormal basis of patch eigenvectors. Thus, transform thresholding (or a Wiener filter) is made on these local orthogonal bases. In this paper a simple patch-based Bayesian method is proposed, which on the one hand keeps most interesting features of former methods, and on the other hand slightly improves the state of the art of color images.