Dirt self-checking method of digital camera image sensor based on retina perception

A technology of image sensor and digital camera, applied in television, image communication, electrical components, etc., can solve problems such as uneven exposure, instability, and inability to overcome vignetting, and achieve the effect of a simple and effective frame

A technology of image sensor and digital camera, applied in television, image communication, electrical components, etc., can solve problems such as uneven exposure, instability, and inability to overcome vignetting, and achieve the effect of a simple and effective frame

CN110012287AActive Publication Date: 2019-07-12深圳市创生达电子有限公司
4 Cites 6 Cited by

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Dirt self-checking method of digital camera image sensor based on retina perception
  • Dirt self-checking method of digital camera image sensor based on retina perception
  • Dirt self-checking method of digital camera image sensor based on retina perception

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0087] The dirty self-inspection method of the digital camera image sensor based on retina perception comprises the following steps:

[0088] Step 1, place a plane light source in front of the digital camera lens, the plane light source completely covers the field of view of the digital camera lens, take pictures with the digital camera, and obtain image data of a frame of RGB channel (such as figure 2 Shown: 1 is a plane light source; 2 is a lens group of a digital camera; 3 is an image sensor of a digital camera);

[0089] Step 2, performing a priori method grayscale on the image data;

[0090] Step 3. Set a convolution kernel to perform mean value downsampling based on convolution (such as image 3 shown);

[0091] Step 4, performing convolution calculation and self-quotient image (SQI) calculation based on retinal perception, to obtain data in floating point form;

[0092] Step 5, perform data processing to obtain unsigned integer data;

[0093] Step 6. Use the normal...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention relates to a dirt self-checking method of a digital camera image sensor based on retina perception, which comprises the following steps of: 1, placing a pure color object in front of a digital camera lens, and photographing to obtain image data of a frame of RGB channel; 2, reading image data and carrying out graying by a priori method; 3, setting a convolution kernel, and carrying out convolution-based mean value downsampling; 4, performing convolution calculation and retina perception-based self-quotient image calculation to obtain data in a floating point form; 5, performing data processing; 6, performing normalized cross correlation template matching by adopting a normalized cross correlation algorithm to obtain a correlation heat map; 7, performing thresholding processing on the correlation heat map; and 8, analyzing the connected domain to obtain the dirty position of the digital camera image sensor. The method can quickly and effectively extract and detect the dirtcharacteristics of the digital camera image sensor, and is low in detection cost, high in processing speed and easy to implement.

Description

[0001] (1) Technical field [0002] The invention relates to a detection method of an image sensor, in particular to a method for self-detection of dirt of a digital camera image sensor based on retinal perception. [0003] (two), background technology [0004] Image enhancement methods are an essential image processing step in autonomous driving, outdoor robots, aerospace, biomedicine, public safety, AR, VR, and intelligent manufacturing in industry. Before the computer vision algorithm is formally processed, the image enhancement algorithm will be performed on the original image to improve some details in the image, such as tone remapping, edge enhancement, etc. This method is based on stretching the feature part with a small original value range into a larger dynamic range. [0005] Most real-time algorithms are based on artificial modeling, so the algorithm needs to perceive the global grayscale, such as grayscale thresholding, HOG, cluster analysis, FSAT features, etc. D...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
12 Jul 2019
Publication
CN110012287A
IPC
H04N17/00
CPC
H04N17/002
Inventors
刘咏晨; 毕成