Jongseong Choi

Super‐resolution approach to overcome physical limitations of imaging sensors: An overview

Although the performance of CCD and CMOS imaging sensors has improved since their invention, they still have several physical limitations, such as various sources of noise, limited dynamic range, and limited spatial resolution. Besides these physical limitations, they have malfunctioning problems, such as smearing and blooming, which degrade the quality of captured images. These limitations and malfunctioning problems can be overcome, based on device physics and circuit technology. However, a signal‐processing‐based approach is a good alternative solution to these problems, because it may cost less and existing imaging systems can be still utilized. In a broad sense, this signal‐processing‐based approach can be called a super‐resolution approach. The goal of this article is to introduce a super‐resolution approach that overcomes the limitations of imaging sensors. To this purpose, we describe the existing limitations of imaging sensors first, and then describe the corresponding super‐resolution approach.

High Dynamic Range Image Reconstruction with Spatial Resolution Enhancement

For the last two decades, two related approaches have been studied independently in conjunction with limitations of image sensors. The one is to reconstruct a high-resolution (HR) image from multiple low-resolution (LR) observations suffering from various degradations such as blur, geometric deformation, aliasing, noise, spatial sampling and so on. The other one is to reconstruct a high dynamic range (HDR) image from differently exposed multiple low dynamic range (LDR) images. LDR is due to the limitation of the capacitance of analogue-to-digital converter and the nonlinearity of the imaging systems response function. In practical situations, since observations suffer from limitations of both spatial resolution and dynamic range, it is reasonable to address them in a unified context. Most super-resolution (SR) image reconstruction methods that enhance the spatial resolution assume that the dynamic ranges of observations are the same or the imaging systems response function is already known. In this paper, the conventional approaches are overviewed and the SR image reconstruction, which simultaneously enhances spatial resolution and dynamic range, is proposed. The image degradation process including limited spatial resolution and limited dynamic range is modelled. With the observation model, the maximum a posteriori estimates of the response function of the imaging system as well as the single HR image and HDR image are obtained. Experimental results indicate that the proposed algorithm outperforms the conventional approaches that perform the HR and HDR reconstructions sequentially with respect to both objective and subjective criteria.

Modeling and analysis of 3-D solenoid embedded inductors

Investigation of the statistical variation of integrated passive components is crucial for designing and characterizing the performance of multichip module (MCM) substrates. In this paper, the statistical analysis of three-dimensional (3-D) solenoid inductors manufactured in a multilayer low-temperature cofired ceramic (LTCC) process is presented. A set of integrated inductor structures is fabricated, and their scattering parameters are measured for a range of frequencies from 50 MHz to 5 GHz. Using optimized equivalent circuits obtained from HSPICE, mean and absolute deviation is calculated for each component of each device model. Monte Carlo Analysis for the inductor structures is then performed using HSPICE. Using a comparison of the Monte Carlo results and measured data, it is determined that for even a small number of sample structures, the statistical variation of the component values provides an accurate representation of the overall inductor performance.

Noise insensitive focus value operator for digital imaging systems

Most auto-focusing algorithms based on focus values which are calculated with high frequency image components are very sensitive to noise factor. The noise under low illumination conditions generally degrades the auto-focusing performance. In this paper, we propose an operator to determine the noise insensitive focus value for digital imaging systems. The proposed algorithm uses spatially adapted weights based on the local statistics of the image and the noise factors. Experiments conducted with and without noise factors demonstrate the high performance of the proposed method.

Investigation of 3-D embedded inductors using Monte Carlo analysis

The statistical analysis of 3D solenoid inductors manufactured in a multilayer low-temperature cofired ceramic (LTCC) process is presented. A set of integrated inductor structures is fabricated, and their scattering parameters are measured for a range of frequencies from 50 MHz to 5 GHz. Using optimized equivalent circuits obtained from HSPICE, mean and absolute deviation is calculated for each component of each device model. Monte Carlo analysis for the inductor structures is then performed using HSPICE. Using a comparison of the Monte Carlo results and measured data, it is determined that for even a small number of sample structures, the statistical variation of the component values provides an accurate representation of the overall inductor performance.

Construction of super-resolution imaging system considering spatially varying sub-pixel registration

Imaging sensors have physical limitations in spatial resolution, spectral resolution and dynamic range. Super-resolution (SR) image processing technology is to overcome these physical limitations. For decades, numerous researches have been conducted from theoretical points of view, and a variety of high-performance SR algorithms have been proposed. However, there is little work on the implementation of real-world SR imaging system. We have implemented two types of SR imaging systems. First, 9-eye system designed as a prototype is presented, and then 6-eye big system following the prototype is announced and demonstrated. The proposed SR algorithms and a few conventional SR algorithms are applied to both of the SR imaging systems. Problems and further study in SR imaging systems are constructed and discussed through experimental results.