Andrey V. Nasonov

Scale-space method of image ringing estimation

Suppression of ringing effect is a challenging problem. It is mainly caused by absence of effective methods of ringing artifact detection. In this paper we introduce a ringing estimation method based on scale-space analysis. The estimation shows good results for low-pass filtered test images and in adaptive image deringing.

Adaptive total variation deringing method for image interpolation

This paper presents a new adaptive post-processing algorithm for ringing artifact reduction after image interpolation (up sampling). The algorithm is based on the concept of total variation (TV) for ringing control. It uses known TV of the blocks of the low-resolution image. Conditional gradient, subgradient and projection subgradient methods for this algorithm are considered and analyzed. A test set of 181300 overlapping 11times11 blocks of real images was used for local algorithm optimization and analysis. Local conditional gradient method shows the best objective and subjective results.

Adaptive Image Deblurring with Ringing Control

The paper presents an adaptive image deblurring method with ringing control. Images are split in analogy with unsharp mask into low- and high-frequency components. Edges are sharpened in low-frequency domain using deconvolution with Total Variation constraint. High-frequency information is amplified using ringing level control.

Edge quality metrics for image enhancement

The paper presents a new adaptive full reference method for quality measurement of image enhancement algorithms. The method is based on the analysis of basic edges—sharp edges which are distant from another edges. The proposed basic edges metrics calculates error values in two areas related to typical artifacts of image enhancement algorithms: basic edges area and basic edges neighborhood. The metrics are illustrated with an application to image resampling and image deblurring but it is also applicable for image deringing and image denoising.

Fast Super-Resolution Using Weighted Median Filtering

A non-iterative method of image super-resolution based on weighted median filtering with Gaussian weights is proposed. Visual tests and basic edges metrics were used to examine the method. It was shown that the weighted median filtering reduces the errors caused by inaccurate motion vectors.

Edge-preserving nonlinear iterative image resampling method

In this paper, an edge-preserving nonlinear iterative regularization-based image resampling method for a single noise-free image is proposed. Several aspects of the resampling algorithm are investigated: choice of discrepancy and regularization norms, improvements of convergence speed using edge-directional steepest-descent method and patch-based details synthesis. A model of a downsampling operator based on a camera observation model is considered.

Finding areas of typical artifacts of image enhancement methods

The paper presents a new method to find areas related to typical artifacts of image enhancements methods. Two artifacts are analyzed: edge blur and ringing effect. The method is based on the analysis of basic edges—the edges which remain after performing image processing algorithms.

3D Image Sharpening by Grid Warping

A method for sharpening of 3D volume images has been developed. The idea of the proposed algorithm is to transform the 3D neighborhood of the edge so that the neighboring pixels move closer to the edge, and then resample the image from the warped grid to the original pixel grid. The proposed technique preserves image textures while making the edges sharper. The effectiveness of the proposed method is demonstrated with synthetic volume images and real micro CT images.

Ringing Artifact Suppression Using Sparse Representation

The article refers to the problem of ringing artifact suppression. The ringing effect is caused by high-frequency information corruption or loss, it appears as waves or oscillations near strong edges. We propose a novel method for ringing artifact suppression after Fourier cut-off filtering. It can be also used for image deringing in the case of image resampling and other applications where the frequency loss can be estimated. The method is based on the joint sparse coding approach. The proposed method preserves more small image details than the state-of-the-art algorithms based on total variation minimization, and outperforms them in terms of image quality metrics.

Edge-directed image interpolation using color gradient information

Image resampling method using color edge-directed interpolation has been developed. It uses color image gradient to perform the interpolation across image gradient rather than along image gradient. The developed combined method takes color low resolution image and grayscale high resolution image obtained by a non-linear image resampling method as an input. It includes consecutive calculation stages for high resolution color gradient, for high resolution color information interpolation and finally for high resolution color image assembling. The concept of color basic edges is used to analyze the results of color image resampling. Color basic edge points metric was suggested and used to show the effectiveness of the proposed image interpolation method.