Defective pixel detection and correction device and method based on texture recognition

Abstract

The invention provides a defective pixel detection and correction device based on texture recognition. The device comprises an image sensor data input unit for inputting image data of an image sensor;a chromatic aberration compensation unit connected to the image sensor data input unit, and used for counting the chromatic aberration of each pixel of the image data in an m*n data window, selectinga median value of the chromatic aberration to obtain a chromatic aberration value of the current pixel, subtracting the chromatic aberration value, and performing chromatic aberration compensation; adefective pixel detection unit for calculating a total variation of N pixels around the current pixel, including pixels of the same channel or different channels, determining the texture intensity ofthe current pixel according to the calculation result, and detecting defective pixels; and a defective pixel correction unit for collecting the defective pixels according to the detection result of the defective pixel detection unit. The invention further provides a defective pixel detection and correction method based on texture recognition.

Description

technical field

[0001] The present invention relates to the technical field of image processing, in particular to a device and implementation method for detecting and correcting defective pixels based on texture recognition. Background technique

[0002] During the manufacture or operation of the image sensor device, some defective pixels may not be able to accurately sense the light level, so there will be some dead pixels in the output. There are two cases of these dead pixels, fixed dead pixels that are too bright or too dark, and one is that the variance of the temporal change is larger than that of the surrounding pixels. To correct or remove these dead pixels, it is necessary to perform defect pixel correction. In general, the way to correct a defective pixel is to estimate a correct value for a specific defective pixel based on its adjacent pixels, and then compare the estimated value with the initial value. If the difference is greater than the set threshold, it is ...

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