Mun-ki Kang

The noise distribution of images obtained by X-ray sensors in low-dosage situations can be analyzed using the Poisson and Gaussian mixture model. Multiscale conversion is one of the most popular noise reduction methods used in recent years. Estimation of the noise distribution of each subband in the multiscale domain is the most important factor in performing noise reduction, with non-subsampled contourlet transform (NSCT) representing an effective method for scale and direction decomposition. I

A nonlinear regularized iterative image restoration algorithm is proposed, according to which only the noise variance is assumed to be known in advance. The algorithm results from a set theoretic regularization approach, where a bound of the stabilizing functional, and therefore the regularization parameter, are updated at each iteration step. Sufficient conditions for the convergence of the algorithm are derived and experimental results are shown.< <ETX xmlns:mml="http://www.w3.org/1998/Math/Ma

A multispectral filter array (MSFA) image sensor with red, green, blue and near-infrared (NIR) filters is useful for various imaging applications with the advantages that it obtains color information and NIR information simultaneously. Because the MSFA image sensor needs to acquire invisible band information, it is necessary to remove the IR cut-offfilter (IRCF). However, without the IRCF, the color of the image is desaturated by the interference of the additional NIR component of each RGB color

In this correspondence, a constrained least-squares multichannel image restoration approach is proposed, in which no prior knowledge of the noise variance at each channel or the degree of smoothness of the original image is required. The regularization functional for each channel is determined by incorporating both within-channel and cross-channel information. It is shown that the proposed smoothing functional has a global minimizer.

The validity of a multichannel deconvolution technique where both within and between channel relations are used was demonstrated using stochastic and deterministic deconvolution filters. In either case, prior knowledge about the original image and the noise is required. A generalized regularized multichannel image deconvolution approach is proposed in which no prior knowledge of the variance of the noise at each channel or a bound on the high-frequency energy of the image are assumed. This infor

Abstract Although the performance of CCD and CMOS imaging sensors has improved since their invention, they still have several physical limitations, such as various sources of noise, limited dynamic range, and limited spatial resolution. Besides these physical limitations, they have malfunctioning problems, such as smearing and blooming, which degrade the quality of captured images. These limitations and malfunctioning problems can be overcome, based on device physics and circuit technology. Howe

We propose algorithms for estimating the phase of a deterministic signal using its bispectrum. The bispectrum of a signal is the (discrete) Fourier transform of its triple correlation. While second-order statistics (e.g., correlation function, power spectrum, etc.) do not provide any information about the phase of the signal, third-order statistics (e.g., triple correlation, bispectrum, etc.) allow the recovery of the phase of the signal. We showthatthe applicability oftwo commonly used algorith

An important consideration in regularized image restoration is the evaluation of the regularization parameter. Various techniques exist in the literature for the evaluation of this parameter, which depend on the assumed prior knowledge about the problem. These techniques evaluate the regularization parameter either at a separate preprocessing step or by iterating based on the completely restored image, therefore requiring many restorations of the image with different values of the regularization

Recently, several RGB-White (RGBW) color filter arrays (CFAs) have been proposed, which have extra white (W) pixels in the filter array that are highly sensitive. Due to the high sensitivity, the W pixels have better SNR (Signal to Noise Ratio) characteristics than other color pixels in the filter array, especially, in low light conditions. However, most of the RGBW CFAs are designed so that the acquired RGBW pattern image can be converted into the conventional Bayer pattern image, which is then

This paper proposes a novel way of combining color demosaicing and the auto white balance (AWB) method, which are important parts of image processing. Performance of the AWB is generally affected by demosaicing results because most AWB algorithms are performed posterior to color demosaicing. In this paper, in order to increase the performance and efficiency of the AWB algorithm, the color constancy problem is examined during the color demosaicing step. Initial estimates of the directional lumina

For the last two decades, two related approaches have been studied independently in conjunction with limitations of image sensors. The one is to reconstruct a high-resolution (HR) image from multiple low-resolution (LR) observations suffering from various degradations such as blur, geometric deformation, aliasing, noise, spatial sampling and so on. The other one is to reconstruct a high dynamic range (HDR) image from differently exposed multiple low dynamic range (LDR) images. LDR is due to the

In this paper, an algorithm is proposed to improve contrast and saturation without color degradation. The local histogram equalization (HE) method offers better performance than the global HE method, whereas the local HE method sometimes produces undesirable results due to the block-based processing. The proposed contrast-enhancement (CE) algorithm reflects the characteristics of the global HE method in the local HE method to avoid the artifacts, while global and local contrasts are enhanced. Th