Vladimir Fursov

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.

Support subspaces method for synthetic aperture radar automatic target recognition

This article offers a new object recognition approach that gives high quality using synthetic aperture radar images. The approach includes image preprocessing, clustering and recognition stages. At the image preprocessing stage, we compute the mass centre of object images for better image matching. A conjugation index of a recognition vector is used as a distance function at clustering and recognition stages. We suggest a construction of the so-called support subspaces, which provide high recognition quality with a significant dimension reduction. The results of the experiments demonstrate that the proposed method provides higher recognition quality (97.8%) than such methods as support vector machine (95.9%), deep learning based on multilayer auto-encoder (96.6%) and adaptive boosting (96.1%). The proposed method is stable for objects processed from different angles.

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.

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.

Information technology of remotely sensed optical image analysis on the basis of multiscale conceptions integration

The information technology of remotely sensed image analysis is based on system integration of two main concepts: diffractive optical elements (DOEs) aided multispectral preprocessing and multiscale analysis. Proposed technology allows to: decrease the threshold of hidden signal detection; detect the signals with unknown form; improve the performance of subpixel signal detection and estimation in case of limited resolution of sensors. Data preprocessing method for anomaly detection on the base of DOE technology is developed. DOE-based technology is implemented and tested. Nonparametric statistic methods for signal detection and estimation are proposed. Multiresolution image analysis methods are applied for anomaly detection and estimation.

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.

Support subspaces method for recognition of the synthetic aperture radar images using fractal compression

The goal of this work is to develop a technology that can reduce recognition computational complexity with the rise of recognition quality. We use an approach based on implementation of the conjugation indices of the vectors with the class feature spaces. We suggest a new criterion of class separability based on the conjugation index and use it to form so-called support subspaces from the training vectors. This procedure decreases computing complexity at training stage about 1000 times in comparison with previous algorithm implementation and improves recognition quality. The most significant decrease of the computational complexity of the proposed technology is achieved by implementing the fractal compression to radar images. The results prove that using this technology leads to an increase of the recognition quality.

Support subspaces method for fractal images recognition

This paper presents the recognition method of fractal images. The approach is considered based on using support subspaces. Support subspaces are constructed with vectors of source data using a conjunction index. The proposed new computing algorithm for the conjugation index reduces requirements for computing capacities and memory. It is shown that the proposed method of construction, supporting subspaces without vectors with stand-out conjunction index, improves recognition rate with dimension reduction of the source data.