Artem Nikonorov

Correcting color and hyperspectral images with identification of distortion model

Abstract This paper presents a novel identification-based image correction method using a bi-illuminant dichromatic reflection model. Image patches with uniform properties over distorted and distortion-free images or image parts are used as a prior knowledge for identification. We identify the distortion correction function on a set of these patches, called spectrum shape elements, with the Hausdorff metric. The main issue during prior knowledge representation is for each distorted spectrum shape element to find a corresponding distortion-free element. A necessary condition to find a matching spectrum shape element is presented and theoretically proved. Identification problem was solved using a RANSAC-based optimization with this necessary condition as an optimization constraint. The method works well both for color and hyperspectral images. The proposed image correction procedure was tested on a set of color images and AVIRIS hyperspectral remote sensing data and proved to provide the quality superior to the results obtained with Retinex correction.

Fresnel lens imaging with post-capture image processing

This paper describes a unified approach to correct optical distortions in images formed by a Fresnel lens with computational post-processing that opens up new opportunities to use Fresnel lenses in lightweight and inexpensive computer vision devices. Traditional methods of aberration correction do not address artifacts introduced by a Fresnel lens in a systematic way and thus fail to deliver image quality acceptable for general-purpose color imaging. In our approach, the image is restored using three steps: first, by deblurring the base color channel, then by sharpening other two channels, and finally by applying color correction. Deblurring and sharpening remove significant chromatic aberration and are similar to the restoration technique used for images formed by simple refraction lenses. Color correction stage removes strong color shift caused by energy redistribution between diffraction orders of Fresnel lens. This post-capture processing was tested on real images formed by a four-step approximation of the Fresnel lens manufactured in our optics laboratory.

OpenNFT: An open-source Python/Matlab framework for real-time fMRI neurofeedback training based on activity, connectivity and multivariate pattern analysis

Neurofeedback based on real-time functional magnetic resonance imaging (rt-fMRI) is a novel and rapidly developing research field. It allows for training of voluntary control over localized brain activity and connectivity and has demonstrated promising clinical applications. Because of the rapid technical developments of MRI techniques and the availability of high-performance computing, new methodological advances in rt-fMRI neurofeedback become possible. Here we outline the core components of a novel open-source neurofeedback framework, termed Open NeuroFeedback Training (OpenNFT), which efficiently integrates these new developments. This framework is implemented using Python and Matlab source code to allow for diverse functionality, high modularity, and rapid extendibility of the software depending on the users needs. In addition, it provides an easy interface to the functionality of Statistical Parametric Mapping (SPM) that is also open-source and one of the most widely used fMRI data analysis software. We demonstrate the functionality of our new framework by describing case studies that include neurofeedback protocols based on brain activity levels, effective connectivity models, and pattern classification approaches. This open-source initiative provides a suitable framework to actively engage in the development of novel neurofeedback approaches, so that local methodological developments can be easily made accessible to a wider range of users.

Vessel Segmentation for Noisy CT Data with Quality Measure Based on Single-Point Contrast-to-Noise Ratio

This paper describes a comprehensive multi-step algorithm for vascular structure segmentation in CT scan data, from raw slice images to a 3D object, with an emphasis on improving segmentation quality and assessing computational complexity. To estimate initial image quality and to evaluate denoising in the absence of the noise-free image, we propose a semi-global contrast-to-noise quality metric. We show that total variation-based filtering in the \( L_{1} \) metric results in the best denoising when compared to widely used non-local means or anisotropic diffusion denoising. To address higher computational complexity of our denoising algorithm, we created two high performance implementations, using Intel MIC and NVIDIA CUDA and compared results. In combination with proposed nearly real-time incremental segmentation technique, it provides fast and framework with controlled quality.

Fractal recognition of compact artifacts on color images

Analysis of small test fragments or compact artifacts is essential for many color correction problems. An efficient method of analysis is the recognition of compact artifacts. However, pattern recognition by features requires the determination of significant features for each applied problem. An alternative approach to the recognition of compact artifacts, which requires no feature extraction and is based on systems of iterated functions and comparison of their attractors, is proposed.

Detection and color correction of artifacts in digital images

A problem of automated retouching of point artifacts in the pre-press process is considered. A new algorithm of detection and localization of multiple point flares is proposed. The algorithm is based on using the so-called conjugate indicator. A scheme for constructing learning rules for tuning the system for different types of artifacts is developed. An example illustrating the proposed algorithm performance on a real image is given.

Comparative evaluation of deblurring techniques for Fresnel lens computational imaging

With suggested computational post-processing workflow for correcting optical distortions, the Fresnel lens can finally be used in lightweight and inexpensive computer vision sensors. Common methods for image enhancement do not comprehensively address the blurring artifacts caused by strong chromatic aberrations in images produced by a simple Fresnel optical system. To deliver image quality acceptable for general-purpose color imaging, we propose a computational post-capture processing to enhance the quality of images acquired with a 256-level Fresnel lens. The PSNR quality measure is then applied to estimate resulting quality for different deblurring techniques. A novel technique that removes chromatic blur without computationally expensive deconvolution can be considered a breakthrough as it finally enables in-camera embedded post-processing.

Correction of distortions in color images based on parametric identification

The paper under consideration deals with a certain information technology for correction of color digital images distortions. These distortions result from image registration process and interaction with lighting environment. In this study, we consider the basic scheme of the correction technology for various types of distortions. The scheme consists of three main stages: detection of distorted areas, identifying of color distortion model, and constructing the correction color mapping. Efficient algorithms are proposed for each stage. The results obtained for some kinds of distortions are given in the paper.

Real-time fMRI data for testing OpenNFT functionality

Here, we briefly describe the real-time fMRI data that is provided for testing the functionality of the open-source Python/Matlab framework for neurofeedback, termed Open NeuroFeedback Training (OpenNFT, Koush et al. [1]). The data set contains real-time fMRI runs from three anonymized participants (i.e., one neurofeedback run per participant), their structural scans and pre-selected ROIs/masks/weights. The data allows for simulating the neurofeedback experiment without an MR scanner, exploring the software functionality, and measuring data processing times on the local hardware. In accordance with the descriptions in our main article, we provide data of (1) periodically displayed (intermittent) activation-based feedback; (2) intermittent effective connectivity feedback, based on dynamic causal modeling (DCM) estimations; and (3) continuous classification-based feedback based on support-vector-machine (SVM) estimations. The data is available on our public GitHub repository: https://github.com/OpenNFT/OpenNFT_Demo/releases.

Experimental study of optical characteristics of a satellite-based Offner hyperspectrometer

A hyperspectrometer based on the Offner scheme was investigated. Spectral characteristics were studied and calibrated using a standard spectrometer. As a result of estimating the deviations of the spectra of the imaging hyperspectrometer and the reference spectrometer, calibration coefficients were obtained. The reflectance spectra of beets, onions and potatoes under natural solar illumination were experimentally obtained. Based on the analysis of hyperspectral imaging data, an analysis of the distribution of vegetative indices and, in particular, moisture content, was carried out. Analysis of histograms of moisture content index distribution was carried out.