Joo Young Kang

Robust local restoration of space-variant blur image

In this paper, we propose a space variant image restoration method where the each different local regions of a given image are de-blurred by each different estimated de-convolution filter locally. The depth of each local blocks are estimated roughly on the optical module representing different indices of refraction for different wavelengths of light. Following the depth, each different region of an image is restored based on the sharpest channel among 3 channels (Red, Green, Blue). Then, in order to prevent discontinuities between the differently restored image regions, we use the piecewise linear interpolation on overlapping regions. Also, practically, this method is applied to 3Mega camera module in order to confirm the effect of proposed algorithm.

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.

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.

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.