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Color correction is an essential image processing operation that transforms a camera-dependent RGB color space to a standard color space, e.g., the XYZ or the sRGB color space. The color correction is typically performed by multiplying the camera RGB values by a color correction matrix, which often amplifies image noise. In this paper, we propose an effective color correction pipeline for a noisy...
In this work we introduce a variational nonconvex model for color image regularization. We express the variational problem as an instance of the half quadratic algorithm (HQA). Moreover, the generalized HQA allows us to prove convergence of the variational problem. As a demonstrator of our framework, we consider a vectorial total variation (VTV) formulation with an additional nonconvex pair-wise color-channel...
In this paper, we propose an adaptive regularizer learning method in the framework of MAP for low rank approximation (ARLLR). We assume that the prior distribution of the singular values is Laplacian with varying scale parameters. By using a full maximize a posterior (MAP) we learn the optimal scale parameters iteratively. We indicate that ARLLR is equivalent to low rank approximation regularized...
Computed tomography is increasingly enabling scientists to study physical processes of materials at micron scales. The MBIR framework provides a powerful method for CT reconstruction by incorporating both a measurement model and prior model. Classically, the choice of prior has been limited to models enforcing local similarity in the image data. In some material science problems, however, much more...
Although non-local image denoising has attracted much research effort due to its superior performance, little attention has focused on its color extension. Most existing non-local color image denoising methods process the color channels of an input image separately. However, in order to improve the performance of color image denoising, all color channels should be processed jointly for fully utilizing...
Based on a diverse range of priors on natural scene images and noise, numerous denoising algorithms have been proposed in the literature. The image quality resulting from different denoising algorithms may vary significantly across a data set. In this work, we propose a denoising algorithm selection framework that chooses among different denoising algorithms using comparison-based image quality assessment...
In this paper, we propose an image prior based on morphological gradients for image recovery. The morphological gradient is defined as the difference between dilation and erosion of an image and approximates the image gradient. This prior provides regularization with an 𝐿1-𝐿∞ norm. The regularization problem with the proposed prior is reduced to a constrained minimization problem and is solved by...
In this paper we describe a straightforward, yet effective method of recovering angles from a set of tomographic projections when the view-angles are completely unknown. Existing works on this problem have consistently assumed availability of projections from a large number of angles as well as made assumptions on the underlying distribution of angles to aid reconstruction. We make no such assumptions,...
Ultrasonography is an important tool and has been widely used in clinical applications, however, the physicians and surgeons still often suffers great difficulties in diagnosis and treatment due to the high speckle noise of ultrasound images. Existing speckle reduction methods usually over-smooth low contrast features since they are sensitive to contrast variations in the images. In our paper, we...
Prior knowledge plays an important role in image denoising tasks. This paper utilizes the data of the input image to adaptively model the prior distribution. The proposed scheme is based on the observation that, for a natural image, a matrix consisted of its vectorized non-local similar patches is of low rank. We use a non-convex smooth surrogate for the low-rank regularization, and view the optimization...
Recently, low-rank representation (LRR) based methods have been used for hyperspectral image (HSI) denoising, which can simultaneously remove different types of noise: Gaussian noise, impulse noise, dead lines, and so on. However, the LRR based method does not make full use of the spatial information in HSI. In this paper, we integrate the superpixel segmentation (SS) into the LRR, and propose a novel...
We propose a fast, local denoising method where the Euclidean curvature of the noisy image is approximated in a regularizing manner and a clean image is reconstructed from this smoothed curvature. User preference tests show that when denoising real photographs with actual noise our method produces results with the same visual quality as the more sophisticated, nonlocal algorithms Non-local Means and...
Approximate Message Passing (AMP) is an iterative reconstruction algorithm that performs signal denoising within a compressive sensing framework. We propose the use of heavy tailed distribution based image denoising, specifically using a Cauchy prior based Maximum A-Posteriori (MAP) estimate within a wavelet based AMP compressive sensing structure. The use of this MAP denoising algorithm provides...
Denoising and Super-Resolution are two inverse problems that have been extensively studied. Over the years, these two tasks were treated as two distinct problems that deserve a different algorithmic solution. In this paper we wish to exploit the recently introduced Plug-and-Play Prior (PPP) approach [1] to connect between the two. Using the PPP, we turn leading denoisers into super-resolution solvers...
We propose a new denoising algorithm for camera pipelines and other photographic applications. We aim for a scheme that is (1) fast enough to be practical even for mobile devices, and (2) handles the realistic content dependent noise in real camera captures. Our scheme consists of a simple two-stage non-linear processing. We introduce a new form of boosting/blending which proves to be very effective...
Two sequential camera fingerprint detection methods are proposed. Sequential tests implement a log-likelihood ratio test in an incremental way, thus enabling a reliable decision with a minimal number of observations. One of our methods adapts Goljan et al.'s to sequential operation. The second, which offers better performance in terms of average number of test observations, is based on treating the...
Annihilating filer-based low rank Hankel matrix (ALOHA) approach was recently proposed as an intrinsic image model for image inpainting estimation. Based on the observation that smoothness or textures within an image patch are represented as sparse spectral components in the frequency domain, ALOHA exploits the existence of annihilating filters and the associated rank-deficient Hankel matrices in...
This paper describes a novel scheme to reduce the quantization noise of compressed videos and improve the overall coding performances. The proposed scheme first consists in clustering noisy patches of the compressed sequence. Then, at the encoder side, linear mappings are learned for each cluster between the noisy patches and the corresponding source patches. The linear mappings are then transmitted...
The advent of depth sensing technologies has eased the detection of object contours in images. For efficient image compression, coded contours can enable edge-adaptive coding techniques such as graph Fourier transform (GFT) and arbitrarily shaped sub-block motion prediction. However, acquisition noise in captured depth images means that detected contours also suffer from errors. In this paper, we...
We propose a novel signal model, based on sparse representations, that captures cross-scale features for visual signals. We show that cross-scale predictive model enables faster solutions to sparse approximation problems. This is achieved by first solving the sparse approximation problem for the downsampled signal and using the support of the solution to constrain the support at the original resolution...
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