hoon Jung

Lens distortion correction and enhancement based on local self-similarity for high-quality consumer imaging systems

In this paper, a novel image enhancement system for a wide-angle lens camera is presented. The proposed system consists of; i) lens distortion correction using space-varying interpolation kernels and ii) image restoration based on the local self-similarity. The correction process for the geometric distortion produced by a wide-angle lens results in radial distortion artifacts caused by non-linear resampling. To reduce such artifacts, the proposed algorithm uses space-varying interpolation kernels derived from the lens calibration data. The corrected image is further enhanced using self-example-based image restoration. Experimental results demonstrate the proposed method can correctly remove the geometric distortion and further enhance the quality of the radially interpolated image.

Real-time super-resolution for digital zooming using finite kernel-based edge orientation estimation and truncated image restoration

This paper presents a novel real-time super-resolution (SR) method using directionally adaptive image interpolation and image restoration. The proposed interpolation method estimates the edge orientation using steerable filters and performs edge refinement along the estimated edge orientation. Bi-linear and bi-cubic interpolation filters are then selectively used according to the estimated edge orientation for reducing jagging artifacts in slanting edge regions. The proposed restoration method can effectively remove image degradation caused by interpolation using the directionally adaptive truncated constrained least-squares (TCLS) filter. The proposed method provides high-quality magnified images which are similar to or better than the result of advanced interpolation or SR methods without high computational load. Experimental results indicate that the proposed system gives higher peak-to-peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) values than the state-of-the-art image interpolation methods.

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.

Frequency-domain analysis of discrete wavelet transform coefficients and their adaptive shrinkage for anti-aliasing

We present an antialiasing method using combined wavelet-Fourier transform and spatially adaptive shrinkage of the transform coefficients. Traditional antialiasing methods employ a simple low-pass filter onto the entire image, so the resulting image loses not only aliasing artifacts but also high-frequency components such as edges and ridges. The proposed algorithm analyzes the property of the LL subband of the discrete wavelet transform (DWT), and reduces aliasing artifacts using patch-adaptive shrinkage of the DWT coefficients. More specifically, an antialiased LL subband is obtained using adaptive patch-based aliasing reduction. To detect an aliased region, we subtract the discrete Fourier transform (DFT) coefficients of the LL subband from the DFT coefficients of antialiased LL subband. The detected aliasing artifacts in the LH, HL, and HH subbands are reduced by patch-wise adaptive shrinkage of the transform coefficients. The resulting antialiased image is obtained using the inverse DWT. The aliasing artifacts can be efficiently reduced by adaptively shrinking wavelet transform coefficients for preserving high-frequency image details. The proposed antialiasing algorithm is suitable for removing aliasing artifacts which frequently occur in imaging sensors with limited resolution.

Lenslet-based Plenoptic Camera Compensation and Efficient Depth Map Estimation for Mobile Applications

In this paper, we present a depth reconstruction method for lenslet-based plenoptic cameras. Previous work in depth reconstruction from light field data was focused on synthetic or non lenslet-based plenoptic cameras. Lenslet-based plenoptic camera uses microlenses to obtain the 4D light field and results in a large non-uniformity across the sensor. A novel correction was applied to shading due to the microlenses by flat field correction. Results using commercially available lenslet-based plenoptic camera are presented with feasibility of real time applications.

Fast rendering of all-in-focus image with high SNR from light field images

Light field camera is gaining its popularity since it allows users to refocus a captured image. But it is not easy to obtain a high quality all-in-focus image even though each sub-image has wide depth-of-field. Existing methods generate refocused images on every focal length, then combine them into a final all-in-focus image by selecting best focused image on every pixel position. This paper proposed a simple and fast method to generate all-in-focus image of which low light performance is very high. The resultant images are all-in-focused and have high SNR simultaneously.

Advanced image sensor technology for pixel scaling down toward 1.0µm (Invited)

As pixel size of image sensors shrinks down rapidly, we are reaching technical barrier to get the required low light performance. In this paper, recent advanced technologies such as backside illumination, new color filter array, low F-number with extended depth of field technologies, etc. are introduced to overcome such a barrier. It is shown that the integration of these advanced sensor technologies can make pixel size shrink down toward 1.0 mum with the required performance.