Jaehwan Jeon

Fully digital auto-focusing system with automatic focusing region selection and point spread function estimation

We present a fully digital auto-focusing (FDAF) system with automatic focusing region selection and a priori estimated dataset of circularly symmetric point-spread functions (PSFs). The proposed approach provides realistic, unsupervised PSF estimation by analyzing the entropy and edge information in the automatically selected focusing region. The main advantage of the proposed system is the fast and robust estimation of a defocusing PSF due to simply selecting the optimal PSF in small, homogeneous region-ofinterest. The proposed FDAF system consists of functional units; i) focusing region selection, ii) PSF selection by generating the major step response in the region from the blurred input image, and iii) image restoration using the selected PSF. Experimental results show the proposed focusing region selection method is more effective than the traditional methods, and the resulting image of the FDAF system provides high visual quality with appropriately amplified details in the image. For this reason, the proposed algorithm can realize low-cost, intelligent focusing function for various image acquisition devices, such as digital cameras, mobile phone cameras, and consumers camcorders.

Robust focus measure for unsupervised auto-focusing based on optimum discrete cosine transform coefficients

In this paper we present a robust focus measure for unsupervised auto-focusing based on optimum discrete cosine transform (DCT) coefficients. DCT has a low computational complexity due to real-valued coefficients, and a small number of coefficients have to be calculated to achieve a good spectral representation of an image. The focusing region of an input image is divided into nonoverlapping 8 × 8 sub-images, and spectra of sub-images are obtained by 8 × 8 DCT. The focusing measure is computed from the middle frequency components. By appropriately chosen DCT coefficients, the proposed focusing measure is robust to Gaussian and impulsive noises. Experimental results show that the proposed method is more effective than the traditional methods based on the comparison with well-known autofocusing uncertainty measure (AUM).

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.

Weighted image defogging method using statistical RGB channel feature extraction

In this paper, we present a weighted adaptive image defogging method by extracting features in the RGB color channels. We adaptively detect an atmospheric light through undesired fog in the dark channel prior obtained in the YCbCr color channels and generate a transmission map based on the detected atmospheric light. We adaptively remove the fog by applying the color correction algorithm based on the feature extraction in the RGB color channels. The proposed algorithm can overcome the problem of local color distortion, which is known to be the limitations of existing defogging techniques. Experimental results demonstrate that the proposed algorithm can remove image degradation caused by fog, clouds, smoke, and dust in digital imaging devices.

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.

Multi-frame example-based super-resolution using locally directional self-similarity

This paper presents a multi-frame superresolution approach to reconstruct a high-resolution image from several low-resolution video frames. The proposed algorithm consists of three steps: i) definition of a local search region for the optimal patch using motion vectors, ii) adaptive selection of the optimum patch based on low-resolution image degradation model, and iii) combination of the optimum patch and reconstructed image. As a result, the proposed algorithm can remove interpolation artifacts using directionally adaptive patch selection based on the low-resolution image degradation model. Moreover, superresolved images without distortion between consecutive frames can be generated. The proposed method provides a significantly improved super-resolution performance over existing methods in the sense of both subjective and objective measures including peak-to-peak signal-to-noise ratio (PSNR), structural similarity measure (SSIM), and naturalness image quality evaluator (NIQE). The proposed multi-frame super-resolution algorithm is designed for realtime video processing hardware by reducing the search region for optimal patches, and suitable for consumer imaging devices including ultra-high-definition (UHD) digital televisions, surveillance systems, and medical imaging systems for image restoration and enhancement.

Real-time spatially adaptive image restoration using truncated constrained least squares filter

A finite impulse response (FIR) filter design method is presented by truncating the constrained least squares filter for real-time, spatially adaptive image restoration. The proposed method truncates the original constrained least squares image restoration filter using the Maxwell-Boltzmann distribution kernel. For the edge preserving image restoration, the orientation of local edge is analyzed based on the covariance matrix, and the edge orientation-adaptive restoration filters are generated. The reduced size of the FIR type restoration filter makes hardware implementation easier for real-time image enhancement. Experimental results show that the proposed method provide more detail and less restoration artifacts than existing methods. As a result, the proposed restoration filter can be applied to realtime image enhancement systems, such as high-definition televisions and video surveillance systems.

Real-time digital zooming for mobile consumer cameras using directionally adaptive image interpolation and restoration

In this digest, we present a novel real-time digital zooming method based on directionally adaptive image interpolation and restoration. The proposed method first estimates an edge direction using steerable filters and performs weighted smoothing along the estimated edge direction. Bi-cubic and bi-linear interpolations are selectively used according to the estimated edge direction. Degradation and artifacts caused by interpolation are removed by employing a directionally adaptive truncated constrained least squares (TCLS) filter. The proposed digital zooming method followed by image restoration provides high-quality magnified images which are similar to the result of computationally intensive super-resolution algorithms. The proposed method can be applied to real-time image processing, and embedded in the form of the finite impulse response (FIR) filtering structure. It is suitable for digital zooming system of mobile phone cameras, tablet PCs, and digital camcorders.

Spatially adaptive image defogging using edge analysis and gradient-based tone mapping

In this paper, we present a spatially ad aptive approach to image d efogging based on edge analysis and gradient-based tone mapping. We adaptively select an atmospheric lightt hrough unde sired fog or cloud in the dark channel prior according to the edge information of an image and generate a transmission map based on the selected a tmospheric light. We a daptively remove the fog using the estimated transmission map and apply tone mapping using gradient values of the image. The proposed algorithm can overcome the problem of local color distortion, which is known to be the limitations of existing defogging techniques. Experimental results demonstrate that the proposed algorithm can i ncrease t echnical co mpetitiveness of consumer i maging devices by removing atmospheric artifacts caused by fog, clouds, smoke, and dust to name a few.

Fast digital zooming system using directionally adaptive image interpolation and restoration.

This paper presents a fast digital zooming system for mobile consumer cameras using directionally adaptive image interpolation and restoration methods. The proposed interpolation algorithm performs edge refinement along the initially estimated edge orientation using directionally steerable filters. Either the directionally weighted linear or adaptive cubic-spline interpolation filter is then selectively used according to the refined edge orientation for removing jagged artifacts in the slanted edge region. A novel image restoration algorithm is also presented for removing blurring artifacts caused by the linear or cubic-spline interpolation using the directionally adaptive truncated constrained least squares (TCLS) filter. Both proposed steerable filter-based interpolation and the TCLS-based restoration filters have a finite impulse response (FIR) structure for real time processing in an image signal processing (ISP) chain. Experimental results show that the proposed digital zooming system provides high-quality magnified images with FIR filter-based fast computational structure.