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This paper presents a novel automatic white balance (AWB) algorithm for consumer imaging devices. While existing AWB methods require reference white patches to correct color, the proposed method performs the AWB function using only an input image in two steps: i) white point detection, and ii) color constancy gain computation. Based on the dark channel prior assumption, a white point or region can be accurately extracted, because the intensity of a sufficiently bright achromatic region is higher than that of other regions in all color channels. In order to finally correct the color, the proposed method computes color constancy gain values based on the Y component in the XYZ color space. Experimental results show that the proposed method gives better color-corrected images than recent existing methods. Moreover, the proposed method is suitable for real-time implementation, since it does not need a frame memory for iterative optimization. As a result, it can be applied to various consumer imaging devices, including mobile phone cameras, compact digital cameras, and computational cameras with coded color.

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.

In this paper, a novel dynamic range extension method is presented by detecting under-exposed regions and performing spatially adaptive contrast enhancement. More specifically, a combined color-channels transmission (CCT) map is proposed to detect low-contrast regions, and spatially adaptive gain computation is used to enhance the contrast of the under-exposed regions. As a result, the proposed method can extend the dynamic range of a single input image by changing the brightness of under-exposed regions with preserving the brightness of background. Moreover, the proposed method requires less computational load than existing multiple frames-based high dynamic range (HDR) imaging methods without color distortion. For these reasons, the proposed method can be applied to various imaging devices, such as mobile phone cameras, compact digital cameras, and video surveillance systems.

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.

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.

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.

Video object segmentation often fails when the background and foreground contain a similar distribution of colours. Proposed is a novel image segmentation algorithm to detect salient motion under a complex environment by combining temporal difference and background generation. Experimental results show that the proposed algorithm provides a twice higher matching ratio than the conventional Gaussian mixture-based approaches under various conditions.