Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Image dead pixel detection and processing method

A processing method and technology of dead pixels, applied in the field of image dead pixels detection and processing, can solve problems such as inability to deal with multiple consecutive bad pixels, high false detection rate of image dead pixels, and poor processing effect of dead pixels

Active Publication Date: 2016-12-07
合肥酷芯微电子有限公司
View PDF7 Cites 20 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Some existing bad pixel detection algorithms either have a relatively high false detection rate of bad pixels in the image, or the bad pixel processing effect is not good, or they cannot handle multiple consecutive bad pixels.

Method used

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
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Image dead pixel detection and processing method
  • Image dead pixel detection and processing method
  • Image dead pixel detection and processing method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0043] refer to Figure 1 to Figure 4 , the image bad point detection and processing method is characterized in that different reference points are selected in the pixel matrix according to the difference in the color of the current pixel point, and the difference value between the reference point and the current pixel point is calculated; the hardware quality of the image sensor is selected. The algorithm can deal with the continuous existence of several bad pixels such as single, two, three, and four; and then judge whether the current pixel is a bad pixel according to different noise models and neighborhood conditions; further according to the set dead pixel strength, Predict whether a few pixels behind the current pixel may also be bad pixels; then judge whether the bad pixel is on the edge according to the amount of change in different directions, and determine the edge direction where the bad point is located; finally, according to whether the bad point is on the edge and...

Embodiment 2

[0045] When the described reference point is selected according to different pixel colors, 8 pixels in 24 pixel points in 3 neighborhoods in the Bayer pixel matrix centered on this point are selected as reference points with the same color as the center pixel point as reference points, and 8 reference points The pixels are recorded op1~op4, op6~op9 respectively from left to right and from top to bottom, and calculate the absolute value of the difference between the reference point and the center pixel pixel value, using the symbol abs to represent the absolute value operation, the specific diff1 is Pixel value I of op1 op1 and the pixel value I of the central pixel point op5 op5 The absolute value of the difference, namely diff1=abs(I op1 -I op5 ); diff2 is the pixel value I of op2 op2 and the pixel value I of the center pixel point op5 op5 The absolute value of the difference, namely diff2=abs(I op2 -I op5 ); diff3 is the pixel value I of op3 op3 and the pixel value I ...

Embodiment 3

[0083] see Figure 5 , firstly in step (000), manually set the dead pixel removal mode, which is set to mode 1 in this embodiment, that is, the method of the present invention is used to process a single bad pixel image. Then (001) takes the current pixel as the center to establish a 5×5 Bayer pixel matrix, such as figure 1 shown;

[0084] next enter Figure 5 Step (010) in the process of selecting points, first judge whether the current pixel is a G pixel point, and select 8 of the 24 pixel points other than the center as reference points in different ways, if figure 1 In the matrix shown, the current pixel point 33 is a G pixel point, then the selection method of the reference pixel matrix is ​​as follows figure 2 shown; if it is not a G pixel, select image 3 Way. Select results for different reference points, such as figure 2 and image 3 As shown, the current pixel is marked as op5, and the nine pixels click figure 2 or image 3 As shown, they are marked as op1...

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 present invention discloses an image dead pixel detection and processing method. The method is mainly used for processing dead pixels in an image, can well process dead pixels at the edge of the image, and can well process multiple dead pixels. Firstly, according to a condition of a hardware image sensor, a condition that the method processes most continuous dead pixels is selected; then the dead pixels are processed row by row through iteration, a pixel before the current pixel is a reliable pixel, the current pixel is the center, and a matrix of surrounding 5*5Bayer pixels is established; according to whether the center point is a G pixel, nine different pixels are selected as a reference; according to an image noise threshold, whether the current pixel is a dead pixel is determined; according to difference values between the eight surrounding pixels and the current pixel, whether the pixel is at the edge of the image is determined; and according to different direction edges at which the dead pixels are, different reliable pixels are selected as substituting pixels, and a value is assigned to the current dead pixel so as to correct the dead pixel.

Description

technical field [0001] The invention relates to the technical field of digital image processing, in particular to an image dead point detection and processing method, which has a certain effect on the situation of multiple bad points. Background technique [0002] At present, digital imaging products such as general computer cameras, mobile phone cameras, various digital cameras, and video cameras use image sensors mostly of RGB Bayer type, and obtain images through the Bayer color matrix when collecting images. Due to the current level of manufacturing technology, it is almost inevitable that the image captured by the image sensor will have dead pixels, and when the image sensor device is used for a long time, more dead pixels will be generated in the image due to the use of the image sensor. The appearance of dead pixels will not only reduce the visual effect of the image, but the appearance of multiple dead pixels may even lose some important image information. [0003] ...

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
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): H04N17/00H04N5/367
CPCH04N17/00H04N25/68
Inventor 胡越黎燕明胡云生
Owner 合肥酷芯微电子有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products