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A near-infrared (NIR) band provides information invisible to human eyes for discriminating and recognizing objects more clearly under low lighting conditions. To capture color and NIR images simultaneously, a multispectral filter array (MSFA) sensor is used. However, because lenses have different refractive indices for different wavelengths, lenses may fail to focus all rays to the same convergence. This is the reason an out-of-focus problem occurs and images are blurred. In this paper, a demosaicking algorithm that considers the out-of-focus problem is proposed. This algorithm is used by the MSFA of a red-green-blue-NIR image sensor to obtain color and NIR images. After the energies of the multispectral (MS) channels in the MSFA image are balanced to minimize aliasing, that image is filtered by the estimated low-pass kernel to generate a panchromatic (PAN) image. When an image is acquired, the out-of-focus problem and the formation process of the PAN image are modeled. The desired MS image is estimated by solving the least squares approach of the difference between the PAN and MS images based on the models. The experimental results show that the proposed algorithm performs well in estimating high-quality MS images and reduces the out-of-focus problem.

We propose a contrast enhancement algorithm considering surrounding information by illumination image. Conventional contrast enhancement techniques can be classified as a retinex-based method and a tone mapping function-based method. However, many retinex methods suffer from high-computational costs or halo artifacts. To cope with these problems, efficient edge-preserving smoothing methods have been researched. Tone mapping function-based methods are limited in terms of enhancement since they are applied without considering surrounding information. To solve these problems, we estimate an illumination image with local adaptive smoothness, and then utilize it as surrounding information. The local adaptive smoothness is calculated by using illumination image properties and an edge-adaptive filter based on the just noticeable difference model. Additionally, we employ a resizing method instead of a blur kernel to reduce the computational cost of illumination estimation. The estimated illumination image is incorporated with the tone mapping function to address the limitations of the tone mapping function-based method. With this approach, the amount of local contrast enhancement is increased. Experimental results show that the proposed algorithm enhances both global and local contrasts and produces better performance in objective evaluation metrics while preventing a halo artifact.

Recently, color filter array patterns based on optimal design by reducing interchannel aliasing have been developed. Although these optimally subsampled patterns produce less degradation of resolution for the images in daylight about 6500 K, aliasing and grid effect become more prominent under the different lighting conditions. Moreover, the color interpolation (CI) for Bayer pattern cannot be utilized for these patterns. Since it is impossible to change coated patterns according to various conditions, CI algorithms have to compensate for light variations. In this letter, a method for generating intermediate quincuncial pattern is proposed for two patterns. Also, edge adaptive interpolation for the quincuncial pattern is carried out. Experimental results show that the proposed method reduces aliasing and grid effect.

When a digital image sequence is sampled with a periodically emitting light source, the color rolling (CR) phenomenon occurs, which is shown by periodical variations of color and luminance values. In conventional CR suppression (CRS) methods, color variation has been reduced by using auto white balance methods. However, the CR phenomenon still appears in the resulting image sequences due to interfield illuminant intensity variation. In the proposed CRS method, the interfield luminance and color variations are simultaneously suppressed by estimating the illuminant change between the current and the target fields. In order to consider the object motions, a motion detection technique is used to estimate the luminance changes that occurred due to the CR phenomenon. Moreover, the illuminant color is estimated using the CR achromatic color distribution in the chromaticity space which is founded on the periodicity of the CR phenomenon. Based on the motion detection and the achromatic color detection techniques, the illuminant is estimated using the obtained color components in a common area of both static and achromatic regions. The experimental results demonstrate that our strategy efficiently suppresses the CR phenomenon without being affected by moving objects and produces luminance and color constant image sequences.

We propose a chromatic aberration (CA) reduction technique that removes artifacts caused by lateral CA and longitudinal CA, simultaneously. In general, most visible CA-related artifacts appear locally in the neighborhoods of strong edges. Because these artifacts usually have local characteristics, they cannot be removed well by regular global warping methods. Therefore, we designed a nonlinear partial differential equation (PDE) in which the local characteristics of the CA are taken into account. The proposed algorithm estimates the regions with apparent CA artifacts and the ratios of the magnitudes between the color channels. Using this information, the proposed PDE matches the gradients of the edges in the red and blue channels to the gradient in the green channel, which results in an alignment of the positions of the edges while simultaneously performing a deblurring process on the edges. Experimental results show that the proposed method can effectively remove even significant CA artifacts, such as purple fringing as identified by the image sensor. The experimental results show that the proposed algorithm achieves better performance than existing algorithms.

We propose a global motion (GM) compensated preprocessing algorithm for block-based frame rate conversion (FRC). The proposed method estimates camera motions such as zooms or rotations between two input frames, and accordingly, produces nontranslational GM-free pictures by performing GM compensation with respect to the temporal location where the FRC method reconstructs an intermediate frame. To reduce the computational load, the proposed method first subsamples input images, and then block-wise motion estimation (ME) is performed. With the ME results, the proposed method detects scene changes and nontranslational GMs. This allows us to determine whether to proceed with the GM estimation and compensation processes. The geometric motion model is adopted to describe camera motions with four parameters, and these values are iteratively found on a motion vector (MV) field. Experimental results show that the proposed algorithm achieves significant performance improvements of subsequent FRC methods.

We propose a motion-compensation-based deinterlacing algorithm using global and representative local motion estimation. The proposed algorithm first divides an entire image into five regions of interest (ROIs) according to the temporally predicted motion type (i.e., global or local) and the spatial position. One of them is for global motion estimation and the others are for local motion estimation. Then, dominant motions of respective ROIs are found by adaptive projection approach. The adaptive projection method not only estimates dominant local motions with low computational cost, but also ensures consistent global motion estimation. Using the estimated motion vectors, adaptive two-field bidirectional motion compensation is performed. The arbitration rules, measuring the reliability of motion compensation accurately, produce high-quality deinterlaced frames by effectively combining the results of motion compensation and the stable intrafield deinterlacing. Experimental results show that the proposed deinterlacing algorithm provides better image quality than the existing algorithms in both subjective and objective measures.

A deinterlacing algorithm based on edge-dependent interpolation (EDI) that considers edge patterns is proposed. Generally, EDI algorithms perform visually better than other deinterlacing algorithms using one field. However, they produce unpleasant results due to failure in estimating edge direction. To estimate the edge direction precisely, not only simple differences between adjacent two lines but also edge patterns are used. Edge patterns, which give sufficient information to estimate the edge direction, appear along the edge direction. Therefore, we analyze properties of edge patterns and model them as a weight function. The weight function helps the proposed method to estimate the edge direction precisely. Experimental results indicate that the proposed algorithm outperforms conventional EDI approaches with respect to both objective and subjective criteria.