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

Abstract

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.

Dense depth map generation using a single camera with hybrid auto-focusing

“…Therefore, the proposed method measures the difference with the hierarchical phase correlation matching from the feature position of G e (x, y) as shown in Fig. 2 [2]. Finally, the proposed method performs the K-means segmentation to simplify G(x, y) and the image interpolation using the Laplacian matting method [3].…”

“…Thus, a robust feature extraction is needed to decrease computational complexity in low light and noisy condition. The proposed method uses the mixed method with the difference of Gaussian and the image pyramidbased subtraction [2]. In order to reduce the noise and jagging artifact, the feature-extracted image is defined as…”

“…Moreover, the black masks are installed between photodiodes and micro lenses as shown in Fig. 1(b) [2]. Thus, the left-and right-phase image are obtained by sampling each right-and left-masked photodiode.…”

Bioinspired Artificial Eyes: Optic Components, Digital Cameras, and Visual Prostheses

“…The remarkable imaging features of the human eye (i.e., wide FoV of up to 95°, high resolution of 1 arcmin per line pair at the fovea, active accommodation/adaptation to the light environment, and simple configuration) originate from the unusual refractive index distribution that reduces aberrations in the crystalline lens, a focal length tunability by the crystalline lens and ciliary body, and the hemispherical retina that facilitates a wide FoV and low aberrations . In contrast to the human eye, the absence of materials and techniques to assemble a graded refractive index (GRIN) lens, focal length‐tunable lens, and curvilinear image sensors forces the selection of a multilens configuration and electrical/mechanical/optical subsystems as strategies of modern optics …”

Multifocus Image Fusion Using a Sparse and Low-Rank Matrix Decomposition for Aviator’s Night Vision Goggle

“…For the configuration and design, refer to the document [1]. At present the autofocusing methods can be divided into active autofocusing and passive autofocusing [4]. Active autofocusing involves installing external infrared or other tools to measure distance between camera lens and target.…”