Auto focus (AF) method and system for electro-hydraulic (EH) lens with adjustable focus, and an electronic device

Patent metadata

Field	Value
Publication number	US-12368952-B2
Application number	US-202318534020-A
Country	US
Kind code	B2
Filing date	Dec 8, 2023
Priority date	Aug 28, 2023
Publication date	Jul 22, 2025
Grant date	Jul 22, 2025

Abstract

The present disclosure provides an auto focus (AF) method and system for an electro-hydraulic (EH) lens with an adjustable focus, and an electronic device, and relates to the field of lens focusing technologies. The method mainly includes: determining a state space, an action space, and a reward function of a reinforcement learning (RL) method; constructing sample data based on the foregoing determined state space, action space, and reward function, and storing the sample data in an experience pool; and using sample data in the experience pool as training data of a deep neural network (DNN) architecture when there are M sets of the sample data in the experience pool, and obtaining a trained AF policy with reference to a deep deterministic policy gradient (DDPG) algorithm and a single hill climbing optimization (HCO) algorithm.

First claim

What is claimed is:

1. An auto focus (AF) method for an electro-hydraulic (EH) lens with an adjustable focus, comprising:
determining a state space, an action space, and a reward function of a reinforcement learning (RL) method, wherein the state space comprises at least two parameters, respectively being image definition and a camera focal length; the action space comprises one parameter, being a focusing current value of the EH lens with an adjustable focus; and the reward function is a function designed according to an image difference before and after an action;
obtaining a target image acquired by an image sensor, automatically selecting a focusing target area from the target image by using a computer vision technology, and calculating image definition and a camera focal length of the focusing target area;
inputting a current state into a policy network in a deep neural network (DNN) architecture to obtain a current initial action, adding noise to the current initial action to obtain a current composite action, and determining a next state according to the current composite action, wherein the current state refers to image definition and a camera focal length of a current focusing target area; the current initial action refers to a current focusing current value; and the next state refers to image definition and a camera focal length of a next focusing target area;
calculating a current reward according to the current state, the next state, the current composite action, and the reward function, and storing the current state, the next state, the current composite action, and the current reward as a set of sample data in an experience pool;
using sample data in the experience pool as training data of the DNN architecture when there are M sets of the sample data in the experience pool, and obtaining a trained AF policy with reference to a deep deterministic policy gradient (DDPG) algorithm and a single hill climbing optimization (HCO) algorithm; and
deploying the trained AF policy to a to-be-focused EH lens with an adjustable focus, so that the to-be-focused EH lens with an adjustable focus is capable of automatically adjusting a focal point in a real-time environment.

2. The AF method for an EH lens with an adjustable focus according to claim 1 , wherein the state space further comprises image contrast and edge definition.

3. The AF method for an EH lens with an adjustable focus according to claim 1 , wherein the reward function is:

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Assignees

• Univ Jining
• Univ Shanghai

Inventors

• Liu Jun
• Li Hengyu
• Jiang Yunyun
• Liu Jingyi
• Wang Yueying
• Xie Shaorong
• Luo Jun

Classifications

• G02B3/14

  of variable focal length · CPC title

• H04N23/617

  Upgrading or updating of programs or applications for camera control · CPC title

• G06V2201/07

  Target detection · CPC title

• G06V10/22

  by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition · CPC title

• G06V10/82

  using neural networks · CPC title