The Experts below are selected from a list of 276 Experts worldwide ranked by ideXlab platform
Joon Ki Paik - One of the best experts on this subject based on the ideXlab platform.
-
Removal of underwater turbidity using an optical imaging platform
2016 International Conference on Electronics Information and Communications (ICEIC), 2016Co-Authors: Vijay Kumar Gowda, Vivek Maik, K. Karibassappa, Joon Ki PaikAbstract:To design and study under water imaging system in this paper we will use CODE V optical simulator. The effect of underwater turbidity will be studied with the help of optical parameters such as Modulation Transfer Function (MTF), Optical Transfer Function (OTF), and Point Spread Function (PSF). Point spread function (PSF) option computes the characteristics of the images of point objects including the effects of diffraction. It is used when the structure of polychromatic images are aberrated and to analyze it when it is out of focus. These test parameters will be then used for the design and implementation of Restoration Filter that will remove under water distortion completely. The prior estimated parameters will be used in a Bayesian Restoration Filter using least square approach. Experimental results show how the proposed algorithm is better than existing methods both qualitatively and quantitatively.
-
UHD TV image enhancement using example-based spatially adaptive image Restoration Filter
Displays, 2015Co-Authors: Seokhwa Jeong, Jaehwan Jeon, Joon Ki PaikAbstract:Abstract This paper presents a novel image Restoration algorithm using examples and truncated constrained least squares (TCLS) Filter for ultra-high definition (UHD) television systems. The proposed approach consists of three steps: (i) generation of the patch dictionary using multiple-step image blurring, (ii) selection of the optimum patch based on the orientation and the amount of blurring, and (iii) combination of the selected patch in the dictionary and its Filtered version by the TCLS Restoration Filter for reducing the patch mismatch error. In the proposed algorithm, a complicated point-spread-function (PSF) estimation process is replaced with the generation of multiple, differently blurred patches. Furthermore, the patch dictionary is made by orientation-based classification to reduce the time to search the optimum patch. Experimental results show that the proposed algorithm can restore more natural images with less synthetic artifacts than existing methods. The proposed method provides a significantly improved Restoration performance over existing methods in the sense of both subjective and objective measures including peak-to-peak signal-to-noise ratio (PSNR) and structural similarity measure (SSIM).
-
Fast Image Restoration for Spatially Varying Defocus Blur of Imaging Sensor
Sensors, 2015Co-Authors: Hejin Cheong, Eunjung Chae, Gwanghyun Jo, Joon Ki PaikAbstract:This paper presents a fast adaptive image Restoration method for removing spatially varying out-of-focus blur of a general imaging sensor. After estimating the parameters of space-variant point-spread-function (PSF) using the derivative in each uniformly blurred region, the proposed method performs spatially adaptive image Restoration by selecting the optimal Restoration Filter according to the estimated blur parameters. Each Restoration Filter is implemented in the form of a combination of multiple FIR Filters, which guarantees the fast image Restoration without the need of iterative or recursive processing. Experimental results show that the proposed method outperforms existing space-invariant Restoration methods in the sense of both objective and subjective performance measures. The proposed algorithm can be employed to a wide area of image Restoration applications, such as mobile imaging devices, robot vision, and satellite image processing.
-
Spatially adaptive video Restoration using truncated constrained least-squared Filter
The 18th IEEE International Symposium on Consumer Electronics (ISCE 2014), 2014Co-Authors: Vivek Maik, Joon Ki Paik, Hejin Cheong, Eunjung Chae, Gwanghyun Jo, Chanyong ParkAbstract:In this paper, an adaptive video Restoration method is presented for removing spatially-varying blur using truncated constrained least-squared (TCLS) Filter. The proposed method consists of two modules: i) spatially-varying blur estimation based on blur map optimization in temporally adjacent frames and ii) adaptive image Restoration using TCLS Filter according to the estimated blur parameters. The proposed method can restore a video without artifacts by estimating the optimal spatially varying blur map, and the use of the TCLS Restoration Filter enables fast video Restoration. Experimental results show that the proposed method can better restore the test video by 1.2 times in the sense of peak-to-peak signal-to-noise ratio (PSNR).
-
ICCE - Real-time spatially adaptive image Restoration using truncated constrained least squares Filter
2014 IEEE International Conference on Consumer Electronics (ICCE), 2014Co-Authors: Jaehwan Jeon, Joon Ki PaikAbstract:A finite impulse response (FIR) Filter design method is presented by truncating the constrained least squares Filter for real-time, spatially adaptive image Restoration. The proposed method truncates the original constrained least squares image Restoration Filter using the Maxwell-Boltzmann distribution kernel. For the edge preserving image Restoration, the orientation of local edge is analyzed based on the covariance matrix, and the edge orientation-adaptive Restoration Filters are generated. The reduced size of the FIR type Restoration Filter makes hardware implementation easier for real-time image enhancement. Experimental results show that the proposed method provide more detail and less Restoration artifacts than existing methods. As a result, the proposed Restoration Filter can be applied to realtime image enhancement systems, such as high-definition televisions and video surveillance systems.
Akira Hirose - One of the best experts on this subject based on the ideXlab platform.
-
Performance of entire-spectrum-processing complex-valued neural-network Filter to generate digital elevation model in interferometric radar
2018 International Joint Conference on Neural Networks (IJCNN), 2018Co-Authors: Kohei Oyama, Akira HiroseAbstract:Recently we proposed a singular-unit Restoration Filter based on complex-valued neural networks (CVNN) that deal with spatial spectrum in interferometric synthetic aperture radar. We named it entire-spectrum-processing CVNN (ESP-CVNN) Filter. This Filter utilizes more neural generalization ability than other conventional methods. It shows a higher performance with a smaller or almost the same processing time. In this paper, we analyze the relationship between its performance and geographic conditions to be processed. Experiments reveal that the ESP-CVNN Filter is superior to conventional Filters in particular when an observation area has higher density of phase singular points.
-
adaptive phase singular unit Restoration with entire spectrum processing complex valued neural networks in interferometric sar
Electronics Letters, 2018Co-Authors: Kohei Oyama, Akira HiroseAbstract:A singular-unit Restoration Filter based on complex-valued neural networks that deal with spatial spectrum in interferometric synthetic aperture radar is proposed. This Filter utilises more neural generalisation ability than conventional methods. In experiments, it shows a higher accuracy as well as shorter processing time than conventional real-space Filters and shorter learning time than previous spectral-domain learning Filters.
V.k. Ingle - One of the best experts on this subject based on the ideXlab platform.
-
Adaptive image Restoration using the multichannel recursive least squares algorithm
[Proceedings] ICASSP 91: 1991 International Conference on Acoustics Speech and Signal Processing, 1991Co-Authors: M. Chellali, V.k. IngleAbstract:Because of the inhomogeneous nature of images in the real world, and fading imaging systems, an adaptive approach to image Restoration is used. The approach is based on the multichannel adaptive equalization technique. The adaptive algorithm used is the multichannel recursive least squares algorithm, which is an extension of the 1-D recursive least squares (RLS) transversal Filter. This new approach does not require knowledge of system characteristics or signal data; however, a training phase is required for initial adaptation of Restoration Filter coefficients. To verify the proposed approach, simulation results are included.
Norman S Kopeika - One of the best experts on this subject based on the ideXlab platform.
-
Satellite image Restoration Filter comparison
Propagation and Imaging through the Atmosphere III, 1999Co-Authors: Dan Arbel, Norman S KopeikaAbstract:Many properties of the atmosphere affect the quality of images propagating through it by blurring it and reducing its contrast, as well as blur. Use of the standard Wiener Filter for correction of atmospheric blur is often not effective because, although aerosol MTF (modulation transfer function) is rather deterministic, turbulence MTF is random. The atmospheric Wiener Filter is one method for overcoming turbulence jitter. The recently developed atmospheric Wiener Filter, which corrects for turbulence blur, aerosol blur, and path radiance simultaneously, is implemented here in digital Restoration of Landsat TM (thematic mapper) imagery over seven wavelength bands of the satellite instrumentation. Turbulence MTF is calculated from meteorological data or estimated if no meteorological data were measured. Aerosol MTF is consistent with optical depth. The product of the two yields atmospheric MTF, which is implemented in the atmospheric Wiener Filter. Restoration improves both smallness of size of resolvable detail and contrast. Restorations are quite apparent even under clear weather conditions. Techniques for high resolution Restoration involving more versatile Filtering techniques, such as Kalman's and adaptive methods, are considered by Filter comparison.
-
general Restoration Filter for vibrated image Restoration
Applied Optics, 1998Co-Authors: Adrian Stern, Norman S KopeikaAbstract:Mechanical vibrations are often the principal cause of image degradation. Low temporal-frequency mechanical vibrations involve random image degradation that depends on the instant of exposure. Exact Restoration requires the calculation of a specific Filter unique to each vibrated image. To calculate the Restoration Filter for each image, one needs the specific optical transfer function unique to the motion in the image. Therefore the instant of exposure and the motion function have to be measured or estimated by some other means. We develop a Restoration Filter for individual images blurred randomly by low-frequency mechanical vibrations. The Filter is independent of the instant of exposure. The Filter is designed to give its best performance averaged over a complete ensemble of vibrated images. Although when applying the new Filter to any vibrated image the Restoration achieved is slightly poorer than that achieved with an exact Filter unique to the specific motion function, the new Filter has the advantage of simplicity. © 1998 Optical Society of America OCIS code: 100.3020.
-
general Restoration Filter for vibrated image Restoration
Applications of digital image processing. Conference, 1997Co-Authors: Adrian Stern, Norman S KopeikaAbstract:Low temporal frequency vibrations involve random image degradation depending on the instant of exposure. Exact Restoration requires calculating a specific Filter unique to each vibrated image. In order to calculate the Restoration Filter for each image the specific degradation function is needed. Therefore, the instant of exposure has to be measured or estimated by some other means. In this work a general Restoration Filter for a single vibrated image is developed. The Filter is independent of the instant of exposure. Assuming that receiving each image from a vibrated image ensemble is equally likely, the Filter is designed to give best performance averaged over the ensemble. Even though when applying the new Filter to any vibrated image the Restoration achieved is slightly poorer than with an exact Filter based on the specific motion function, the new Filter has the advantage of simplicity. Since the instant of exposure is not needed a new appropriate Filter does not have to be calculated for each exposure, the proposed Filter is more practical and suitable for real time Restoration.
Kohei Oyama - One of the best experts on this subject based on the ideXlab platform.
-
Performance of entire-spectrum-processing complex-valued neural-network Filter to generate digital elevation model in interferometric radar
2018 International Joint Conference on Neural Networks (IJCNN), 2018Co-Authors: Kohei Oyama, Akira HiroseAbstract:Recently we proposed a singular-unit Restoration Filter based on complex-valued neural networks (CVNN) that deal with spatial spectrum in interferometric synthetic aperture radar. We named it entire-spectrum-processing CVNN (ESP-CVNN) Filter. This Filter utilizes more neural generalization ability than other conventional methods. It shows a higher performance with a smaller or almost the same processing time. In this paper, we analyze the relationship between its performance and geographic conditions to be processed. Experiments reveal that the ESP-CVNN Filter is superior to conventional Filters in particular when an observation area has higher density of phase singular points.
-
adaptive phase singular unit Restoration with entire spectrum processing complex valued neural networks in interferometric sar
Electronics Letters, 2018Co-Authors: Kohei Oyama, Akira HiroseAbstract:A singular-unit Restoration Filter based on complex-valued neural networks that deal with spatial spectrum in interferometric synthetic aperture radar is proposed. This Filter utilises more neural generalisation ability than conventional methods. In experiments, it shows a higher accuracy as well as shorter processing time than conventional real-space Filters and shorter learning time than previous spectral-domain learning Filters.