Sung-Chan Park

Motion artifact-free HDR imaging under dynamic environments

High dynamic range (HDR) imaging is one of the most important emerging fields of the next generation digital cameras. It is hard to handle a problem so-called ghosting artifact caused by camera shake and/or object motion in the method of fusing a set of differently exposed images. Some object motions around under or over saturation region still produce severe artifacts due to the reference images dynamic range limitation. For the commercial product, it is the important problem to be solved completely. We analyze this problem and propose a new HDR deghosting scheme capable of dealing with various motions. In order to avoid the ghosting artifacts, we capture only two uncompressed Bayer raw images with different exposures, select the wider dynamic range image as a reference, and process them in the Bayer domain. The experimental results show that our proposed method provides motion artifact-free under dynamic environments with various moving objects.

A digital ISO expansion technique for digital cameras

Markets demands of digital cameras for higher sensitivity capability under low-light conditions are remarkably increasing nowadays. The digital camera market is now a tough race for providing higher ISO capability. In this paper, we explore an approach for increasing maximum ISO capability of digital cameras without changing any structure of an image sensor or CFA. Our method is directly applied to the raw Bayer pattern CFA image to avoid non-linearity characteristics and noise amplification which are usually deteriorated after ISP (Image Signal Processor) of digital cameras. The proposed method fuses multiple short exposed images which are noisy, but less blurred. Our approach is designed to avoid the ghost artifact caused by hand-shaking and object motion. In order to achieve a desired ISO image quality, both low frequency chromatic noise and fine-grain noise that usually appear in high ISO images are removed and then we modify the different layers which are created by a two-scale non-linear decomposition of an image. Once our approach is performed on an input Bayer pattern CFA image, the resultant Bayer image is further processed by ISP to obtain a fully processed RGB image. The performance of our proposed approach is evaluated by comparing SNR (Signal to Noise Ratio), MTF50 (Modulation Transfer Function), color error ∝E*ab and visual quality with reference images whose exposure times are properly extended into a variety of target sensitivity.

Fast transform-based adaptive beamformer for medical ultrasound imaging

Minimum Variance (MV) beamforming has been studied for high resolution ultrasonic imaging. However, it is not easy for the MV beamformer to be implemented into a real time diagnostic system, because it requires too many computations for covariance matrix inversion. We introduce a transform-based adaptive beamforming algorithm for ultrasound medical imaging where the transformation can reduce the dimension of the covariance matrix in estimating beamformation weights. Moreover, it is shown that beamformation result can be directly calculated in the transformed domain. Experimental results indicate that our beamforming method shows better resolution and contrast under a real phantom and in-vivo environment.

Fast non-blind deconvolution based on 2D point spread function database for real-time ultrasound imaging

In the ultrasound medical imaging system, blurring which occurs after passing through ultrasound scanner system, represents Point Spread Function (PSF) that describes the response of the ultrasound imaging system to a point source distribution. So, de-blurring can be achieved by de-convolving the images with an estimated of PSF. However, it is hard to attain an accurate estimation of PSF due to the unknown properties of the tissues of the human body through the ultrasound signal propagates. In addition to, the complexity is very high in order to estimate point spread function and de-convolve the ultrasound image with estimated PSF for real-time implementation of ultrasound imaging. Therefore, conventional methods of ultrasound image restoration are based on a simple 1D PSF estimation [8] that axial direction only by restoring the performance improvement is not in the direction of Lateral. And, in case of 2D PSF estimation, PSF estimation and restoration of the high complexity is not being widely used. In this paper, we proposed new method for selection of the 2D PSF (estimated PSF of the average speed sound and depth) simultaneously with performing fast non-blind 2D de-convolution in the ultrasound imaging system. Our algorithm works on the beam-formed uncompressed radio-frequency data, with pre-measured and estimated 2D PSFs database from actual probe used. In the 2d PSF database, there are pre-measured and estimated 2D PSFs that classified the each different depth (about 5 different depths) and speed of sound (about 1450 or 1540m/s). Using a minimum variance and simple Weiner filter method, we present a novel way to select the optimal 2D PSF in pre-measured and estimated 2D PSFs database that acquired from the actual transducer being used. For de-convolution part with the chosen PSF, we focused on the low complexity issue. So, we are using the Weiner Filter and fast de-convolution technique using hyper-Laplacian priors [11], [12] which is several orders of magnitude faster than existing techniques that use hyper-Laplacian priors. Then, in order to prevent discontinuities between the differently restored each depth image regions, we use the piecewise linear interpolation on overlapping regions. We have tested our algorithm with vera-sonic system and commercial ultrasound scanner (Philips C4-2), in known speed of sound phantoms and unknown speeds in vivo scans. We have applied a non-blind de-convolution with 2D PSFs database for ultrasound imaging system. Using the real PSF from actual transducer being used, our algorithm produces a better restoration of ultrasound image than de-convolution by simulated PSF, and has low complexity for real-time ultrasound imaging. This method is robust and easy to implement. This method may be a realistic candidate for real-time implementation.

Low-light imaging method with visible-band and wide-band image pair

Images captured from digital camera suffer noise due to high sensor gain under dark conditions. Furthermore, camera-shake is unavoidable when exposure time is long enough to have desirable SNR. This paper presents an image capturing method that enhances image quality in low-light environments by fusing visible-band (400 ∼ 700 nm) image and wide-band (400 ∼ 1000 nm) image. Wide-band image has lower noise, richer detail and a monotonic color. Visible-band image has higher noise, poorer detail and a trichromatic color. We describe how the advantages of these images are exploited to create new image that is of higher quality. Experiments demonstrate that the proposed method significantly improves quality of images under low illumination conditions.

Parametric phase information based 2D Cepstrum PSF estimation method for blind de-convolution of ultrasound imaging

In the ultrasound imaging system, blurring which occurs after passing through ultrasound scanner system, represents point spread function (PSF) that describes the response of the ultrasound imaging system to a point source distribution. So, de-blurring can be achieved by de-convolving the ultrasound images with an estimated of corresponding PSF. However, it is hard to attain an accurate estimation of PSF due to the unknown properties of the tissues of the human body through the ultrasound signal propagates. In this paper, we present a new method for PSF estimation in the Fourier domain (FD) based on parametric minimum phase information, and simultaneously, it performs fast 2D de-convolution in the ultrasound imaging system. Although most of complex cepstrum methods [14], are obtained using complex 2D phase unwrapping [18] [19] in order to estimate the FD-phase information of PSF, our algorithm estimates the 2D PSF using 2D FD-phase information with the parametric weighting factor α and β. They affect the feature of PSF shapes.This makes the computations much simpler and the estimation more accurate. Our algorithm works on the beam-formed uncompressed radio-frequency data, with pre-measured and estimated 2D PSFs database from actual probe used. We have tested our algorithm with vera-sonic system and commercial ultrasound scanner (Philips C4-2), in known speed of sound phantoms and unknown speeds in vivo scans.

Realistic fetus skin color processing for ultrasound volume rendering

This paper proposes realistic fetus skin color processing using a 2D color map and a tone mapping function (TMF) for ultrasound volume rendering. The contributions of this paper are a 2D color map generated through a gamut model of skin color and a TMF that depends on the lighting position. First, the gamut model of fetus skin color is calculated by color distribution of baby images. The 2D color map is created using a gamut model for tone mapping of ray casting. For the translucent effect, a 2D color map in which lightness is inverted is generated. Second, to enhance the contrast of rendered images, the luminance, color, and tone curve TMF parameters are changed using 2D Gaussian function that depends on the lighting position. The experimental results demonstrate that the proposed method achieves better realistic skin color reproduction than the conventional method.

Moving object-High Dynamic Range Imaging (HDRI) for artifact-free digital camera

We present a new artifact-free HDRI technology for a consumer digital camera that is based on de-ghosting with a dual-brightness mapping. The proposed approach reduces motion artifacts and the number of capturing images.

Active Motion High Dynamic Range Imaging for digital still camera

Active Motion High Dynamic Range Imaging (HDRI) is a new artifact-free HDRI technology for a consumer digital still camera. This technology allows us to optimize capturing time and to remove motion artifacts by a camera motion and a moving object. To optimize the capturing time, the key component of our approach is a dual-brightness mapping to detect and compensate the artifacts with just two images. This paper shows that the proposed HDRI approach effectively corrects the artifacts.