Jaehyun Im

Single image-based ghost-free high dynamic range imaging using local histogram stretching and spatially-adaptive denoising

In this paper, we present a novel high dynamic range (HDR) imaging method using a single image. The existing multiple image-based HDR methods work only on condition that there is no camera and object movement when acquiring multiple, differently exposed low dynamic range (LDR) images. To overcome such an unrealistic restriction, we make three LDR images from a single input image using local histogram stretching. An edge-preserving spatially adaptive denoising method is also proposed to suppress the noise that is amplified in the histogram stretching process. Because the proposed method self-generates three histogram-stretched LDR images from a single input image, ghost artifacts that are the result of the relative motion between the camera and objects during exposure time, are inherently removed. Therefore, the proposed method can be applied to mobile imaging devices such as a mobile phone camera and a consumer compact camera to provide the ghost artifacts free HDR function in the form of either embedded or post-processing software.

Improved elastic registration for removing ghost artifacts in high dynamic imaging

In this paper, we present a ghost artifacts removing method for obtaining artifact-free high dynamic range (HDR) images in the presence of camera movement. The existing HDR methods work on condition that there is no camera movement when acquiring multiple low dynamic range (LDR) images. For overcoming such unrealistic restriction, we register the multiple LDR images by using a novel target frame finding method. Ghost artifacts are removed in the resulting HDR images since the registration process of LDR images compensates undesired camera motion. Since the proposed method can embed the self compensating function into an imaging device, Ghost-free HDR imaging application can be extended to most consumer cameras, such as mobile phone cameras, point-and-shoot cameras, and digital single lens reflected (DSLR) cameras without using additional stabilizing equipments.

Low-Light Image Enhancement Using Adaptive Digital Pixel Binning.

This paper presents an image enhancement algorithm for low-light scenes in an environment with insufficient illumination. Simple amplification of intensity exhibits various undesired artifacts: noise amplification, intensity saturation, and loss of resolution. In order to enhance low-light images without undesired artifacts, a novel digital binning algorithm is proposed that considers brightness, context, noise level, and anti-saturation of a local region in the image. The proposed algorithm does not require any modification of the image sensor or additional frame-memory; it needs only two line-memories in the image signal processor (ISP). Since the proposed algorithm does not use an iterative computation, it can be easily embedded in an existing digital camera ISP pipeline containing a high-resolution image sensor.

Dark channel prior-based spatially adaptive contrast enhancement for back lighting compensation

In this paper, we present a novel contrast enhancement method for backlit images that consists of three steps: i) computation of the transmission coefficients using the dark channel prior, ii) generation of multiple images having different exposures based on the transmission coefficients, and iii) image fusion. Compared to global intensity transformation methods and spatially invariant contrast enhancement algorithms, our approach first extracts under-exposed regions using the dark channel prior map, and then performs spatially adaptive contrast enhancement. As a result, the contrast of the image is increased, especially for backlit scenes and those with very wide dynamic range, while still preserving image details and color.

Flicker removal for CMOS wide dynamic range imaging based on alternating current component analysis

This paper presents a novel wide dynamic range system to reduce flickering artifact produced by a high speed electronic rolling shutter in a CMOS image sensor. The proposed algorithm removes flicker artifact in the shortexposure image using a correlation between the pair of two differently exposed images to extend the dynamic range. Since the proposed method overcomes the electronic shutter problem in a conventional wide dynamic range imaging system, it can remove the flicker artifact under an alternating current light source such as a fluorescent bulb.

Defocus-invariant image registration for phase-difference detection auto focusing

This paper presents a defocus-invariant image registration method for measuring the shifting value between two differently located patterns in an imaging sensor. Existing registration methods fail with unfocused images since features or regions of interest are degraded by defocus. In order to solve this problem, the proposed method consists of three stages: i) pre-generation of the set of point spread functions (PSFs) estimated in different focusing positions, ii) the geometric transformation estimation using estimated PSF data, and iii) registration using estimated transformation matrix. The proposed method improves out-of-focus degradation through estimation of PSF. For this reason, the proposed method can accurately estimate the difference of phase between two out-of-focus images. Furthermore, it can be applied to phase-difference detection auto focusing, and provide accurate auto focusing performance.

Dark channel prior-based white point estimation for automatic white balance

A novel white point estimation method is presented for automatic white balance (AWB). The proposed method first detects white points by estimating dark channel prior information in the input image, and then adjusts the white balance on the detected white points. Experimental results indicate that the proposed method produces visually-correct color representation in most test images.

Part-template matching-based target detection and identification in UAV videos

Detecting and identifying targets in aerial images has been a challenging problem due to various types of image distortion factors, such as motion of a sensing device, weather variation, scale changes, and dynamic viewpoint. For accurate, robust recognition of objects in unmanned aerial vehicle (UAV) videos, we present a novel target detection and identification algorithm using part-template matching. The proposed method for target detection partitions the target into part-templates by efficient extraction method based on target part regions. We also propose distribution distance measurement-based target identification using the target part-template.

Geometrical transformation-based ghost artifacts removing for high dynamic range image

In this paper, we propose a ghost artifacts removing algorithm for obtaining artifact-free high dynamic range (HDR) images in the presence of camera movement. Existing HDR methods work on condition that there is no camera movement when acquiring multiple low dynamic range (LDR) images. For overcoming such unrealistic restriction, the proposed algorithm first specifies the target and the source images in the set of acquired LDR images, and then the source image is transformed to fit the target image by estimating translation and rotation components in the affine matrix. Therefore, the proposed algorithm can reconstruct the HDR image without ghost artifacts because camera movement is completely removed in transformed images. Experimental results show that the proposed algorithm successfully removes ghost artifacts. For this reason, the proposed algorithm can extend application areas of HDR imaging to various mobile imaging devices, such as a handheld camcorder and a mobile phone camera.

Ghost-free high-dynamic range imaging using layered exposed images based on local histogram equalization

In this paper, we present a novel high dynamic range (HDR) imaging method using a single input image. Conventional multiple image-based HDR methods are successful only on condition that there is no motion in the scene during the acquisition of multiple, differently exposed low dynamic range (LDR) images. If these constraints are not satisfied, a ghost artifact is produced in the resulting HDR image. In order to overcome these limitations, we generate multiple, differently exposed LDR images from a single input image. We call these multiple images a set of layered exposed (LE) images. In order to generate an appropriate set of LE images, the proposed method divides input image into 9 subregions and computes local mean in each subregion to estimate the minimum and maximum local mean. The estimated local means become ranges of histogram equalization (HE). HDR image is generated by fusing differently exposed LE images. More specifically, given a set of LE images, we perform weighted fusion to produce the resulting HDR image, which is inherently free from ghost artifacts since all LE images are geometrically identical. Experimental results show that the proposed method outperforms the existing algorithms in the sense of both removing ghost artifacts and enhancement of image contrast.