• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.52 no.7
• /
• pp.434-441
• /
• 2003

This paper proposes a new quantitative and objective image quality metric which is essential to verify the performance of block-based DCT image coding. The proposed metric considers not only global distortion of coded image such as spatial frequency sensitivity and channel masking using HVS based multi-channel model, but also structural distortions caused block-based coding. The experimental results show a strong correlation between proposed metric and subjective metric.

• The Transactions of the Korean Institute of Electrical Engineers C
• /
• v.52 no.7
• /
• pp.434-434
• /
• 2003

This paper proposes a new quantitative and objective image quality metric which is essential to verify the performance of block-based DCT image coding The proposed metric considers not only global distortion of coded image such as spatial frequency sensitivity and channel masking using HVS based multi-channel model, but also structural distortions caused block-based coding. The experimental results show a strong correlation between propose(B metric and subjective metric.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.29 no.2C
• /
• pp.272-282
• /
• 2004

This paper introduces a regularized mixed norm multi-channel image restoration algorithm using both within-and between- channel deterministic information. For each channel a functional which combines the least mean squares (LMS), the least mean fourth (LMF), and a smoothing functional is proposed. We introduce a mixed norm parameter that controls the relative contribution between the LMS and the LMF, and a regularization parameter defining the degree of smoothness of the solution, where both parameters are updated at each iteration according to the noise characteristics of each channel. The novelty of the proposed algorithm is that no knowledge of the noise distribution for each channel is required and that the parameters mentioned above are adjusted based on the partially restored image.

• The Journal of Korea Robotics Society
• /
• v.17 no.3
• /
• pp.314-321
• /
• 2022

Depth information used in SLAM and visual odometry is essential in robotics. Depth information often obtained from sensors or learned by networks. While learning-based methods have gained popularity, they are mostly limited to RGB images. However, the limitation of RGB images occurs in visually derailed environments. Thermal cameras are in the spotlight as a way to solve these problems. Unlike RGB images, thermal images reliably perceive the environment regardless of the illumination variance but show lacking contrast and texture. This low contrast in the thermal image prohibits an algorithm from effectively learning the underlying scene details. To tackle these challenges, we propose multi-channel remapping for contrast. Our method allows a learning-based depth prediction model to have an accurate depth prediction even in low light conditions. We validate the feasibility and show that our multi-channel remapping method outperforms the existing methods both visually and quantitatively over our dataset.

• Proceedings of the KSRS Conference
• /
• v.1
• /
• pp.282-285
• /
• 2006

This paper analyzed the characteristics of the Lossy JPEG of the meteorological satellite image, and analyzed the quality of the Lossy JPEG compression, which is proper for the LRIT(Low Rate Information Transmission) to be serviced to the SDUS(Small-scale Data Utilization Station) system of the COMS(Communication, Oceans, Meteorological Satellite). Since COMS is to start running after 2008, we collected the data of the MTSAT-1R(Multi-functional Transport Satellite -1R) for analysis, and after forming the original image to be used to LRIT by each channel and time zone of the satellite image data, we set the different quality with the Lossy JPEG compression, and compressed the original data. For the characteristic analysis of the Lossy JPEG, we measured PSNR(Peak Signal to Noise Rate), compression rate and the time spent in compression following each quality of Lossy JPEG compression. As a result of the analysis of the satellite image data of the MTSAT-1R, the ideal quality of the Lossy JPEG compression was found to be 90% in the VIS Channel, 85% in the IR1 Channel, 80% in the IR2 Channel, 90% in the IR3 Channel and 90% in the IR4 Channel.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.5
• /
• pp.2366-2395
• /
• 2018

Multi-view super-resolution (MVSR) refers to the process of reconstructing a high-resolution (HR) image from a set of low-resolution (LR) images captured from different viewpoints typically by different cameras. These multi-view images are usually obtained by a camera array. In our previous work [1], we super-resolved multi-view LR images via image fusion (IF) and blind deblurring (BD). In this paper, we present a new MVSR method that jointly realizes IF and BD based on an integrated energy function optimization. First, we reformulate the MVSR problem into a multi-channel blind deblurring (MCBD) problem which is easier to be solved than the former. Then the depth map of the desired HR image is calculated. Finally, we solve the MCBD problem, in which the optimization problems with respect to the desired HR image and with respect to the unknown blur are efficiently addressed by the alternating direction method of multipliers (ADMM). Experiments on the Multi-view Image Database of the University of Tsukuba and images captured by our own camera array system demonstrate the effectiveness of the proposed method.

• Journal of Magnetics
• /
• v.22 no.2
• /
• pp.238-243
• /
• 2017

In this research, we report the preliminary results of an insertional type parallel transmission (pTx) array that has 7-elements that are placed in the space above a patient table as a transmit (Tx) coil to give an RF transmission ($B_1{^+}$) field for the body object of a 3 Tesla (T) MRI system. In previous research, we have tried to compare the performances of different coil elements and array geometries for a pTx body image. Based on these results, we attempt to obtain a human image with the proposed pTx array. Through the simulation and experimental results, we introduce a possible structure of multi-channel Tx array and verify the utility of a multi-channel Tx body image using $B_1{^+}$ shimming. The insertional pTx array, combined with a receiver (Rx) array coil, provides an enhanced $B_1{^+}$ field homogeneity in a large ROI image as a result of $B_1{^+}$ shimming applied over the full body size object. Through this research, we hope to determine the usefulness of the proposed insertional type RF coil combination for 3 T body imaging.

• The Transactions of the Korean Institute of Electrical Engineers C
• /
• v.50 no.1
• /
• pp.17-22
• /
• 2001

The floating-gate MOSFETs were fabricated by employing 1.5 m n-well CMOS process and their optical-electrical properties were characterized for the application to CMOS image sensor system. Based on the simulation of energy band diagram and operating mechanism of parasitic BJT were proposed as solutions for the increase of photo-current value. In order to realize them, MOSFETs having multi-gate and channel structures were fabricated and 60% increase in photo-current was achieved through enlargement of depletion layer and parallel connection of parasitic BJTs by channel division.

• Journal of Advanced Marine Engineering and Technology
• /
• v.39 no.6
• /
• pp.649-655
• /
• 2015

In this paper, we propose a defogging algorithm for a single image. Dark channel prior (DCP), which is a well-known defogging algorithm, can cause halo artifacts on boundary regions, low-contrast defogging images, and requires a large computational time. To solve these problems, we use histogram information with DCP on transmission estimation regions and a multi-resolution method. Local histogram information can reduce the low-contrast problem on a defogging image, and the multi-resolution method with edge information can reduce the total computational time and halo artifacts. We validate the proposed method by performing experiments on fog images, and we confirm that the proposed algorithm is more efficient and superior than conventional algorithms.

• International Journal of Internet, Broadcasting and Communication
• /
• v.15 no.4
• /
• pp.74-83
• /
• 2023

In this paper, we propose an image compression scheme which uses a demosaicking network and a channel-wise decoder in the decoding network. For the demosaicing network, we use as the input a colored mosaiced pattern rather than the well-known Bayer pattern. The use of a colored mosaiced pattern results in the mosaiced image containing a greater amount of information pertaining to the original image. Therefore, it contributes to result in a better color reconstruction. The channel-wise decoder is composed of multiple decoders where each decoder is responsible for each channel in the color image, i.e., the R, G, and B channels. The encoder and decoder are both implemented by wavelet based auto-encoders for better performance. Experimental results verify that the separated channel-wise decoders and the colored mosaic pattern produce a better reconstructed color image than a single decoder. When combining the colored CFA with the multi-decoder, the PSNR metric exhibits an increase of over 2dB for three-times compression and approximately 0.6dB for twelve-times compression compared to the Bayer CFA with a single decoder. Therefore, the compression rate is also increased with the proposed method than with the method using a single decoder on the Bayer patterned mosaic image.