Sergey Bibikov

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.

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.

Computational Correction for Imaging through Single Fresnel Lenses

The lenses of modern single lens reflex (SLR) cameras may contain a dozen or more individual lens elements to correct aberrations. With processing power more readily available, the modern trend in computational photography is to develop techniques for simple lens aberration correction in post-processing. We propose a similar approach to remove aberrations from images captured by a single imaging Fresnel lens. The image is restored using three-stage deblurring of the base color channel, sharpening other and then applying color correction. The first two steps are based on the combination of restoration techniques used for restoring images obtained from simple refraction lenses. Color correction stage is necessary to remove strong color shift caused by chromatic aberrations of simple Fresnel lens. This technique was tested on real images captured by a simple lens, which was made as a three-step approximation of the Fresnel lens. Promising results open up new opportunities in using lightweight Fresnel lenses in miniature computer vision devices.

Reconstruction of 3D scenes using stereoimages without rectification

The problem of reconstructing 3D scenes using stereopairs of images is considered. In contrast to traditional technologies whereby the rectification and disparity map construction are implemented, the corresponding points are determined directly on the epipolar lines that belong to the given epipolar plane. A complete three-dimensional model of an object is constructed by scanning a 3D scene using pencils of planes. The elimination of fragment-matching errors occurs by using the decimation filtering of the found points. The results of experiments that show the efficiency of the proposed technology are provided.

Memory access optimization in recurrent image processing algorithms with CUDA

The present paper deals with the algorithms of image processing using CUDA technology. Memory optimizations are the most important area for performance of a CUDA application. This is especially urgent for the recurrent algorithms of data processing. The paper proposes a new approach for recurrent data processing on the GPU. The effectiveness of this approach is shown for the moving average algorithm. The proposed algorithms were used to develop image processing software system. Some results obtained for this system are given in the paper.