Jeong Ho Shin

Regularized Interative Image Interpolation and its application to Spatially Scalable Coding

This paper presents a regularized iterative image interpolation algorithm, which can restore high frequency details in the original high resolution image. In order to apply the regularization approach to the interpolation procedure, we first present a two-dimensional separable image degradation model for a low resolution imaging system. According to the model, we propose a regularization based spatial image sequence interpolation algorithm and apply the proposed algorithm to a spatially scalable coding.

Object-based analysis of motion blur and its removal by considering occluded boundaries

An image frame in image sequences, in general, suffers from degradation due to spatially varying motions. In this paper, we propose a new image degradation model for space-variant motion blur and a spatially adaptive image restoration algorithm to remove such motion blur. For the proposed image degradation model, we mathematically analyze boundary effect which arises on the border of two image segments with different motions. We extend the already proposed model for a moving object in an arbitrary direction. In order to represent the point spread function (PSF) for motion blur in an arbitrary direction, we develop a method which distributes energy of samples in the PSF into the neighboring integer grids. In order to remove the above mentioned motion blur, we propose an object-based adaptive regularized image restoration algorithm. Both in synthetically and naturally motion blurred images, the proposed image restoration algorithm gives acceptable performance in removing motion blur and, as a result, in restoring important features, such as numbers and characters, which cannot be recognized in the input blurred image.

Comparison between Wiener and CLS image restoration techniques for multichannel images

Recently, multichannel image restoration gains population in many applications such as color imaging systems, image sequences, etc. In the present paper, we classify many multichannel restoration techniques as Wiener and CLS approaches, which are respectively based on stochastic and deterministic models of the original image. We also compare two approaches in the sense of both restoration performance and implementational efficiency.

Image fusion-based adaptive regularization for image expansion

This paper presents a regularized image sequence interpolation algorithm, which can restore high frequency details by fusing low-resolution frames. Image fusion algorithm gives the feasibility of using different data sets, which correspond to the same scene to get a better resolution and information of the scene than the one obtained using only one data set. Based on the mathematical model of image degradation, we can have an interpolated image which minimizes both residual between the high resolution and the interpolated images with a prior constraint. In addition, by using spatially adaptive regularization parameters, directional high frequency components are preserved with efficiently suppressed noise. The proposed algorithm provides a better-interpolated image by fusing low-resolution frames. We provide experimental results which are classified into non-fusion and fusion algorithms. Based on the experimental results, the proposed algorithm provides a better interpolated image than the conventional interpolation algorithms in the sense of both subjective and objective criteria. More specifically, the proposed algorithm has the advantage of preserving high frequency components and suppressing undesirable artifacts such as noise.

Fast superresolution for image sequences using motion adaptive relaxation parameters

This paper presents a novel fast superresolution algorithm using motion adaptive relaxation parameters. By adopting the fast iterative algorithm based on pre-conditioner, the proposed algorithm can rapidly restore high resolution details in the original high resolution image. Moreover, the proposed algorithm provides a better interpolated images by using the motion adaptive relaxation parameter which controls the convergence rate. Finally, it can be applied to the general image sequence with differently moving objects. As a result, we can obtain high resolution frames from image sequences in almost real-time.

Generalized image degradation model for removing motion blur in image sequence

Moving pictures inevitably suffer from motion blur caused by the relative motion between a camera and objects. Such degradation is due to the nonideal operation of a shutter in the imaging instrument. Especially, motion blur is usually space-variant because the moving objects have different orientation and velocity. The purpose of this paper is to give a precise model of the space-variant motion blue, and to propose an adaptive image restoration techniques which removes the corresponding motion blur. The main concern of the proposed model is to explain the phenomenon that occurs in the boundary region of moving objects. In the process of estimating the degradation, a hierarchical motion estimation method is used. And then an adaptive restoration algorithm shows subjectively acceptable performance in eliminating the space-variant motion blur.

Enhancement of out-of-focus images using fusion-based PSF estimation and restoration

In this paper, we propose an enhancement algorithm of out-of- focused images using fusion-based Point-spread-function (PSF) estimation and restoration. The proposed algorithm can make in-focused image by using only digital image processing techniques, and it requires neither infrared light/ultrasound nor focusing lens assembly operated by electrically powered movement of focusing lens. In order to increase accuracy in estimating the PSF of the defocus image, the proposed algorithm finds true and linear edges by using Canny edge detector, which is optimal edge detector and has good localization, estimates the step response across the edge for each pixel, computes the one-dimensional step response by averaging the step responses, estimates the two-dimensional PSF from the averaged step response, and then provides in- focused image by image restoration filter based on the estimated PSF. Finally, we execute fusion process, which can enhance the quality of the fused image by fusing restored images. There is a limit of the amount of out-of-focus, which can be recovered by the proposed algorithm. Moreover, the proposed algorithm is operating under assumption that an input image contains at least one piece-wise linear boundary between an object and background. In spite of above-mentioned limitations, the proposed algorithm can make acceptable quality of focused image by using only digital image processing.

Region selectable auto-focusing system by digital PSF estimation

A region-selectable fully digital auto-focusing system is proposed. After a region of interest is selected, the amount of out-of-focus in the region is estimated, and then the focused image is made by using a digital image restoration technique. If the selected region does not have valid step-edge, the system does not perform restoration. The proposed system can provide a convenient human interface for a digital auto-focusing system.

General framework of image sequence interpolation

Image interpolation is widely used in various image processing applications. In this paper, we propose a general framework for performing image sequence interpolation and a novel image sequence interpolation technique using a spatio- temporal processing, which can magnify an image with higher resolution than conventional methods. The proposed algorithm is also shown to efficiently reduce noise by using motion compensated temporal filtering. The efficiency of the proposed algorithm is demonstrated through several experimental results.

Regularized iterative image sequence interpolation with spatially adaptive constraints

We propose an image interpolation algorithm based on the regularized iterative image restoration method. By adopting spatial adaptive constraints in each regularized iteration step, the proposed algorithm provides a better interpolated image from the image sequence than the conventional non-adaptive interpolation algorithms. Due to its spatially adaptive nature, it can be applied to the general image sequence with differently moving objects. Real-time implementation of the proposed algorithm is also considered by updating the motion compensated image frames.