• IEIE Transactions on Smart Processing and Computing
• /
• v.1 no.1
• /
• pp.8-16
• /
• 2012

An image obtained from a low light environment results in a low-exposure problem caused by non-ideal camera settings, i.e. aperture size and shutter speed. Of particular note, the multiple color-filter aperture (MCA) system inherently suffers from low-exposure problems and performance degradation in its image classification and registration processes due to its finite size of the apertures. In this context, this paper presents a novel method for the color enhancement of low-exposure images and its application to color shift model-based MCA system for image refocusing. Although various histogram equalization (HE) approaches have been proposed, they tend to distort the color information of the processed image due to the range limits of the histogram. The proposed color enhancement algorithm enhances the global brightness by analyzing the basic cause of the low-exposure phenomenon, and then compensates for the contrast degradation artifacts by using an adaptive histogram specification. We also apply the proposed algorithm to the preprocessing step of the refocusing technique in the MCA system to enhance the color image. The experimental results confirm that the proposed method can enhance the contrast of any low-exposure color image acquired by a conventional camera, and is suitable for commercial low-cost, high-quality imaging devices, such as consumer-grade camcorders, real-time 3D reconstruction systems, digital, and computational cameras.

• Journal of Astronomy and Space Sciences
• /
• v.25 no.4
• /
• pp.445-458
• /
• 2008

Image chain analysis of synthetic aperture radar (SAR) satellite is one of the primary activities for satellite design because SAR image quality depends on spacecraft bus performance as well as SAR payload. Especially, satellite pointing and stability error make worst effect on the original SAR image quality which is implemented by SAR payload design. In this research, Image chain analysis S/W was developed in order to analyze the SAR image quality degradation due to satellite pointing and stability error. This S/W consists of orbit model, attitude control model, SAR payload model, clutter model, and SAR processor. SAR raw data, which includes total 25 point targets in the scene of $5km{\times}5km$ swath width, was generated and then processed for analysis. High resolution mode (spotlight), of which resolution is 1m, was applied. The results of image chain analysis show that radiometric accuracy is the most degraded due to the pointing error. Therefore, the successful design of attitude control subsystem in spacecraft bus for enhancing the pointing accuracy is most important for image quality.

• Journal of the Korean Physical Society
• /
• v.73 no.11
• /
• pp.1764-1773
• /
• 2018

A-PET is a quad-head PET scanner developed for use in small-animal imaging. The dimensions of its volumetric field of view (FOV) are $46.1{\times}46.1{\times}46.1mm^3$ and the gap between the detector modules has been minimized in order to provide a highly sensitive system. However, such a small FOV together with the quad-head geometry causes image quality degradation. The main factor related to image degradation for the quad-head PET is the mispositioning of events caused by the penetration effect in the detector. In this paper, we propose a precise method for modelling the system at the high spatial resolution of the A-PET using a LOR (line of response) based ML-EM (maximum likelihood expectation maximization) that allows for penetration effects. The proposed system model provides the detection probability of every possible ray-path via crystal sampling methods. For the ray-path sampling, the sub-LORs are defined by connecting the sampling points of the crystal pair. We incorporate the detection probability of each sub-LOR into the model by calculating the penetration effect. For comparison, we used a standard LOR-based model and a Monte Carlo-based modeling approach, and evaluated the reconstructed images using both the National Electrical Manufacturers Association NU 4-2008 standards and the Geant4 Application for Tomographic Emission simulation toolkit (GATE). An average full width at half maximum (FWHM) at different locations of 1.77 mm and 1.79 mm are obtained using the proposed system model and standard LOR system model, which does not include penetration effects, respectively. The standard deviation of the uniform region in the NEMA image quality phantom is 2.14% for the proposed method and 14.3% for the LOR system model, indicating that the proposed model out-performs the standard LOR-based model.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.5
• /
• pp.1814-1828
• /
• 2021

Low-light image enhancement is a key technique to overcome the quality degradation of photos taken under scotopic vision illumination conditions. The degradation includes low brightness, low contrast, and outstanding noise, which would seriously affect the vision of the human eye recognition ability and subsequent image processing. In this paper, we propose an approach based on deep learning and Retinex theory to enhance the low-light image, which includes image decomposition, illumination prediction, image reconstruction, and image optimization. The first three parts can reconstruct the enhanced image that suffers from low-resolution. To reduce the noise of the enhanced image and improve the image quality, a super-resolution algorithm based on the Laplacian pyramid network is introduced to optimize the image. The Laplacian pyramid network can improve the resolution of the enhanced image through multiple feature extraction and deconvolution operations. Furthermore, a combination loss function is explored in the network training stage to improve the efficiency of the algorithm. Extensive experiments and comprehensive evaluations demonstrate the strength of the proposed method, the result is closer to the real-world scene in lightness, color, and details. Besides, experiments also demonstrate that the proposed method with the single low-light image can achieve the same effect as multi-exposure image fusion algorithm and no ghost is introduced.

• Journal of Forest and Environmental Science
• /
• v.40 no.1
• /
• pp.15-23
• /
• 2024

This research aimed to assess the possibility of detecting forest degradation using time-series satellite imagery and three different deep learning-based change detection techniques. The dataset used for the deep learning models was composed of two sets, one based on surface reflectance (SR) spectral information from satellite imagery, combined with Texture Information (GLCM; Gray-Level Co-occurrence Matrix) and terrain information. The deep learning models employed for land cover change detection included image differencing using the Unet semantic segmentation model, multi-encoder Unet model, and multi-encoder Unet++ model. The study found that there was no significant difference in accuracy between the deep learning models for forest degradation detection. Both training and validation accuracies were approx-imately 89% and 92%, respectively. Among the three deep learning models, the multi-encoder Unet model showed the most efficient analysis time and comparable accuracy. Moreover, models that incorporated both texture and gradient information in addition to spectral information were found to have a higher classification accuracy compared to models that used only spectral information. Overall, the accuracy of forest degradation extraction was outstanding, achieving 98%.

• Journal of the Optical Society of Korea
• /
• v.5 no.1
• /
• pp.9-15
• /
• 2001

The ocean Scanning Multi-spectral Imager (OSMI) is a payload on the KOrea Multi-Purpose SATellite (KOMPSAT) to perform worldwide ocean color monitoring f the study of biological oceanography. OSMI performs solar and dark calibrations for on-orbit instrument calibration. The purpose of the solar calibration is to monitor the degradation of imaging performance for each pixel of 6 spectral bands and to correct the degradation effect on OSMI image during the ground station date processing. The design, the operation concept, and the radiometric characteristics of the solar calibration are investigated. A linear model of image response and a solar calibration radiance model are proposed to study the instrument characteristics using the solar calibration data. The performance of spectral responsivity and spatial response uniformity. The first solar calibration data and the analysis results are important references for further study on the on-orbit stability of OSMI response during its lifetime.

• Proceedings of the KSRS Conference
• /
• 2005.10a
• /
• pp.586-588
• /
• 2005

The MSC (Multi Spectral Camera) system is a remote sensing payload to obtain high resolution ground image. This system uses lossy image compression method for &Direct mission& that transmit whole image during one contact. But some image degradation occurred especially at high compression ratio. To reduce this degradation, the MSC uses NUC (Non-uniformity Correction) Unit. This unit correct CCD (Charge Coupled Device)'s high-frequency non-uniformity. So high frequency contents of image can be minimized and whole system SNR can be maximized. But NUC has some disadvantage either. It decreases entire system reliability by adding one electronic system. Adding NUC also led to difficulty of electronic design, assembly and testability. In this paper, the comparison is performed between on-orbit non-uniform correction and on ground correction. by evaluating NUC advantage for the point of view of image quality. Using real MSC parameter and proper model, considerable reference point for the system design came to possible.

• Aerospace Engineering and Technology
• /
• v.8 no.1
• /
• pp.58-65
• /
• 2009

The paper identifies noise characteristics of a nonliner image sensor model which reflects a saturation effect of each detector pixel and extends the result to estimate an image SNR (Signla-to-Noise Ratio) distribution over all the pixels in a detector. In particular, nonlinearity of a pixel is studied from two perspectives of including asymmetry of a noise PDF (Probability Distribution Function) and enhancing a pixel SNR value, in comparison to a linear model. It is noted that the proposed image SNR distribution function is useful to effectively select new optimal operation parameter values: an integration time and an pixel-summing number, even after a launch campaign, assuming sensor gain degradation in orbit or inevitable modification of some operation parameter values due to space contingency.

• International Journal of Aeronautical and Space Sciences
• /
• v.13 no.1
• /
• pp.64-73
• /
• 2012

Pointing stability of high precision observation satellites must satisfy the stringent requirements to perform at a designed level. As even a small vibrational disturbance can result in severe degradation of the optical performance, the effects of inorbit vibrational environment on the performance of optical payload must be predicted and analyzed in the design phase in order to ensure that the requirements imposed on the payload are fully met. In this paper, an integrated framework for the evaluation of the performance of optical payloads is developed. The developed simulation tool comprises of the reaction wheel induced disturbance model, state space model of a structure in modal form and Cassegrain reflector model. The performance degradation of the optical system due to jitter is expressed by using modulation transfer function (MTF) and image simulation. Moreover, vibration isolator model is also added to show the effectiveness of using a vibration isolator for the elimination of the effects of jitter in the acquisition of an image.

• The Journal of Korea Robotics Society
• /
• v.9 no.4
• /
• pp.197-205
• /
• 2014

As computer vision algorithms are developed on a continuous basis, the visual information from vision sensors has been widely used in the context of simultaneous localization and mapping (SLAM), called visual SLAM, which utilizes relative motion information between images. This research addresses a visual SLAM framework for online localization and mapping in an unstructured seabed environment that can be applied to a low-cost unmanned underwater vehicle equipped with a single monocular camera as a major measurement sensor. Typically, an image motion model with a predefined dimensionality can be corrupted by errors due to the violation of the model assumptions, which may lead to performance degradation of the visual SLAM estimation. To deal with the erroneous image motion model, this study employs a local bundle optimization (LBO) scheme when a closed loop is detected. The results of comparison between visual SLAM estimation with LBO and the other case are presented to validate the effectiveness of the proposed methodology.