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.
Highlights
Auto-focusing (AF) is one of the most fundamental techniques for acquiring high-quality images using a digital camera and has evolved for the past few decades [1]
Passive auto-focusing techniques are further classified into contrast detection auto-focusing (CDAF) and phase detection auto-focusing (PDAF) approaches
Since the time-of-flight auto-focusing (ToFAF) method directly computes the distance of an object, it can provide accurate auto-focusing for both low light and flat objects without a sufficient amount of detail
Summary
Auto-focusing (AF) is one of the most fundamental techniques for acquiring high-quality images using a digital camera and has evolved for the past few decades [1]. Passive auto-focusing techniques are further classified into contrast detection auto-focusing (CDAF) and phase detection auto-focusing (PDAF) approaches. The hybrid method roughly moves the focusing lens to the near optimal focusing position using either an active or PDAF technique and accurately moves the focusing lens using CDAF [6]. A manual focusing technique uses a specially-designed imaging sensor to compute the phase difference. In this system, two AF points are differently masked to take different phases. In order to solve these problems, a robust feature extraction from the defocused image and the robust phase detection methods are needed. The proposed system performs multi-scale feature extraction from severely defocused input images and estimates the phase difference using phase correlation.
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