Jae-guyn Lim

Complex wavefront shaping for optimal depth-selective focusing in optical coherence tomography

We report on an approach to exploit multiple light scattering by shaping the incident wavefront in optical coherence tomography (OCT). Most of the reflected signal from biological tissue consists of multiply scattered light, which is regarded as noise in OCT. A digital mirror device (DMD) is utilized to shape the incident wavefront such that the maximal energy is focused at a specific depth in a highly scattering sample using a coherence-gated reflectance signal as feedback. The proof-of-concept experiment demonstrates that this approach enhances depth-selective focusing in the presence of optical inhomogeneity, and thus extends the penetration depth in spectral domain-OCT (SD-OCT).

Depth-enhanced 2-D optical coherence tomography using complex wavefront shaping

We report the enhancement in the obtained signal and penetration depth of 2-D depth-resolved images that were taken by shaping the incident wavefront in optical coherence tomography (OCT). Limitations in the penetration depth and signal to noise ratio (SNR) in OCT are mainly due to multiple scattering, which have been effectively suppressed by controlling the incident wavefront using a digital mirror device (DMD) in combination with spectral-domain OCT. The successful enhancements in the penetration depth and SNR are demonstrated in a wide-range of tissue phantoms, reaching depth enhancement of up to 92%. The hidden structures inside a tissue phantom that could not be seen in conventional OCT are clearly revealed through our proposed system. Its 2-D imaging capability, assisted by further optimization of the system for real-time acquisition speed will boost wide-spread use of OCT for in-vivo tissue diagnosis.

In vivo deep tissue imaging using wavefront shaping optical coherence tomography

Abstract. Multiple light scattering in tissue limits the penetration of optical coherence tomography (OCT) imaging. Here, we present in vivo OCT imaging of a live mouse using wavefront shaping (WS) to enhance the penetration depth. A digital micromirror device was used in a spectral-domain OCT system for complex WS of an incident beam which resulted in the optimal delivery of light energy into deep tissue. Ex vivo imaging of chicken breasts and mouse ear tissues showed enhancements in the strength of the image signals and the penetration depth, and in vivo imaging of the tail of a live mouse provided a multilayered structure inside the tissue.

Low light imaging system with? Expanding spectrum band for digital camera

This paper presents the camera system for increasing sensitivity especially in the low light condition. The camera system consists of two parts. The one is the unique image sensor structure which includes special pixels with broad band spectrum response (White+Near IR) and the other is image processing algorithm which fuses color and White+Near IR (WNIR) information to produce high sensitivity color image. With prototype system we prove the possibility of increasing sensitivity, which is almost 3 times better than conventional RGGB Bayer system.

Improving the spatail resolution based on 4D light field data

This paper presents why the spatial resolution of an image captured by a plenoptic camera can be improved. The plenoptic camera captures the 4D light field (angular and spatial information of light) within a limited 2D sensor and results in reducing 2D spatial resolution due to inevitable 2D angular data. However, for improving the reduced resolution, we propose a novel analysis that 2D angular data contain spatially subpixel-shifted information. Although angular data have been defined as lights set radiated from one spatial point in a scene theoretically, practical angular data captured by plenoptic camera consist of spatially different minuscule area. That is, angular data provide the redundant data used generally by super-resolution techniques. Our experimental results demonstrate the improvement of spatial resolution as well as the existence of spatial information within 2D angular data.

Color correction with edge preserving and minimal SNR decrease using multi-layer decomposition

This paper describes the method related to correcting color distortion in color imaging. Acquiring color images from CMOS or CCD digital sensors can suffer from color distortion, which means that the image from sensors is different from the original image in the color space. The main reasons are the cross-talks between adjacent pixels, the color pigment characteristics mismatch with human perception and infra-red (IR) influx to visible channel or red, green, blue (RGB) due to IR cutoff filter imperfection. To correct this distortion, existing methods use multiplying gain coefficients in each color channel and this multiplication can cause noise boost and loss of detail information. This paper proposes the novel method which can not only preserve color distortion correction ability, but also suppress noise boost and loss of detail information in the color correction process of IR corrupted pixels. In the case of non-IR corruption pixels, the use of image before color correction instead of IR image makes this kind of method available. Specifically the color and low frequency information in luminance channel is extracted from the color corrected image. And high frequency information is from the IR image or the image before color correction. The method extracting the low and high frequency information use multi-layer decomposition skill with edge preserving filters.

Chromatic aberration reduction through optical feature modeling

This paper presents a method of digitally removing or correcting Chromatic Aberration (CA) of lens, which generally occurs in an edge region of image. Based on the information of the lenss and sensors features in camera, it determines CA level and the dominant chrominance of CA and efficiently removes extreme CA such as purple fringe and blooming artifacts, as well as a general CA to be generated at an edge in an image captured by a camera. Firstly, this method includes a CA region sensing part analyzing a luminance signal of an input image and sensing a region having CA. Secondly, the CA level sensing part calculates the weight, which indicates a degree of CA, based on a difference between gradients of color components of the input image. Thirdly, for removing the extreme CA such as purple fringe and blooming artifact which caused by the feature of lens and sensor, it uses 1-D Gaussian filters having different sigma values to get the weight. The sigma value indicates the feature of lens and sensor. And, for removing the general CA, it includes the adaptive filter, based on luminance signal. Finally, by using these weights, final filter will be produced adaptively with the level of CA and lenss and sensors features. Experimental results show the effectiveness of this proposed method.

Complex Wavefront Control for Enhancing Penetration Depth in 2-D Optical Coherence Tomography

We report the enhancement of signal and penetration depth in 2-D imaging in optical coherence tomography by suppressing multiple scattering via complex wavefront shaping. Up to 92% enhancements of the penetration depth were observed for a highly scattering sample.

Restoring the spatial resolution of refocus images on 4D light field

This paper presents the method for generating a refocus image with restored spatial resolution on a plenoptic camera, which functions controlling the depth of field after capturing one image unlike a traditional camera. It is generally known that the camera captures 4D light field (angular and spatial information of light) within a limited 2D sensor and results in reducing 2D spatial resolution due to inevitable 2D angular data. Thats the reason why a refocus image is composed of a low spatial resolution compared with 2D sensor. However, it has recently been known that angular data contain sub-pixel spatial information such that the spatial resolution of 4D light field can be increased. We exploit the fact for improving the spatial resolution of a refocus image. We have experimentally scrutinized that the spatial information is different according to the depth of objects from a camera. So, from the selection of refocused regions (corresponding depth), we use corresponding pre-estimated sub-pixel spatial information for reconstructing spatial resolution of the regions. Meanwhile other regions maintain out-of-focus. Our experimental results show the effect of this proposed method compared to existing method.