njong Yoo

Low-Light Image Enhancement Using Adaptive Digital Pixel Binning.

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.

Sensor-Based Auto-Focusing System Using Multi-Scale Feature Extraction and Phase Correlation Matching

This paper presents a novel auto-focusing system based on a CMOS sensor containing pixels with different phases. Robust extraction of features in a severely defocused image is the fundamental problem of a phase-difference auto-focusing system. In order to solve this problem, a multi-resolution feature extraction algorithm is proposed. Given the extracted features, the proposed auto-focusing system can provide the ideal focusing position using phase correlation matching. The proposed auto-focusing (AF) algorithm consists of four steps: (i) acquisition of left and right images using AF points in the region-of-interest; (ii) feature extraction in the left image under low illumination and out-of-focus blur; (iii) the generation of two feature images using the phase difference between the left and right images; and (iv) estimation of the phase shifting vector using phase correlation matching. Since the proposed system accurately estimates the phase difference in the out-of-focus blurred image under low illumination, it can provide faster, more robust auto focusing than existing systems.

Flicker removal for CMOS wide dynamic range imaging based on alternating current component analysis

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.

Defocus-invariant image registration for phase-difference detection auto focusing

This paper presents a defocus-invariant image registration method for measuring the shifting value between two differently located patterns in an imaging sensor. Existing registration methods fail with unfocused images since features or regions of interest are degraded by defocus. In order to solve this problem, the proposed method consists of three stages: i) pre-generation of the set of point spread functions (PSFs) estimated in different focusing positions, ii) the geometric transformation estimation using estimated PSF data, and iii) registration using estimated transformation matrix. The proposed method improves out-of-focus degradation through estimation of PSF. For this reason, the proposed method can accurately estimate the difference of phase between two out-of-focus images. Furthermore, it can be applied to phase-difference detection auto focusing, and provide accurate auto focusing performance.

Hierarchical blur identification from severely out-of-focus images

This paper proposes a blur identification method from severely out-of-focus images. The proposed blur identification algorithm can be used in digital auto-focusing and image restoration. Since it is not easy to estimate a point spread function (PSF) from severely out-of-focus images, a hierarchical approach is applied in the proposed algorithm. For severe out of focus blur, the proposed algorithm uses an hierarchical approach for estimating and selecting feasible PSF from successive down sampled images. The down sampled images contain more useful edge information for PSF estimation. The feasible PSF selected, can then be reconstructed for original image resolution level by up sampling methods. In order to reconstruct the PSF accurately, a regularized PSF reconstruction algorithm is used. Finally, we can restore the severely blurred image with the reconstructed PSF. Experimental results show that reconstructed PSF by the proposed hierarchical algorithm can efficiently restore severely out-of-focus images.

Minimum Statistics-Based Noise Power Estimation for Parametric Image Restoration

This paper describes a method to estimate the noise power using the minimum statistics approach, which was originally proposed for audio processing. The proposed minimum statisticsbased method separates a noisy image into multiple frequency bands using the three-level discrete wavelet transform. By assuming that the output of the high-pass filter contains both signal detail and noise, the proposed algorithm extracts the region of pure noise from the high frequency band using an appropriate threshold. The region of pure noise, which is free from the signal detail part and the DC component, is well suited for minimum statistics condition, where the noise power can be extracted easily. The proposed algorithm reduces the computational load significantly through the use of a simple processing architecture without iteration with an estimation accuracy greater than 90% for strong noise at 0 to 40dB SNR of the input image. Furthermore, the well restored image can be obtained using the estimated noise power information in parametric image restoration algorithms, such as the classical parametric Wiener or ForWaRD image restoration filters. The experimental results show that the proposed algorithm can estimate the noise power accurately, and is particularly suitable for fast, low-cost image restoration or enhancement applications.

Real-Time Continuous-Scale Image Interpolation with Directional Smoothing

A real-time, continuous-scale image interpolation method is proposed based on a bilinear interpolation with directionally adaptive low-pass filtering. The proposed algorithm was optimized for hardware implementation. The ordinary bi-linear interpolation method has blocking artifacts. The proposed algorithm solves this problem using directionally adaptive low-pass filtering. The algorithm can also solve the severe blurring problem by selectively choosing lowpass filter coefficients. Therefore, the proposed interpolation algorithm can realize a high-quality image scaler for a range of imaging systems, such as digital cameras, CCTV and digital flat panel displays.

Regularized Iterative Restoration of Combined Optical and Color Filter Array Degradation

This paper presents an image formation model of a digital camera with consideration of both optical and the Bayer color filter array (CFA) degradations. Based on the proposed model, a regularized iterative image restoration algorithm is presented to deconvolve the space-variant image degradation. The spatially adaptive version further improves the restoration quality. Hardware implementation issues are briefly summarized, and future modification plan is presented.