Edge self-adapting demosaicing method based on RS-SVM integration

Abstract

The invention provides an edge self-adapting demosaicing method based on RS-SVM integration. The method comprises the implementation steps of (1) acquiring a mosaic image, (2) dividing the mosaic image into an edge area and a smooth area, (3) interpolating the smooth area through a signal correlation method, and (4) interpolating the edge area through an SVM integration method. In the step (4), an original sample set is firstly constructed on a chromatic aberration plane constructed through a color correlation and a chromatic aberration constant principle, then resampling is carried out on the original sample set through a Bagging method, resampled sample features are reduced through a rough set dynamic reduction algorithm, member support vector regression is trained through reduced samples, the chromatic aberration values of points to be interpolated are estimated through the trained member support vector regression, and finally a lost pixel value is calculated. The method can improve the edge features of the small edge area of the image, can restrain a pseudo-color effect or a sawtooth phenomenon, and can improve imaging quality.

Description

technical field [0001] The invention relates to the field of image demosaicing, in particular to an edge adaptive demosaicing method based on RS-SVM integration. Background technique [0002] As a popular color image imaging device, a digital camera is a camera that uses an electronic sensor to convert optical images into electronic data. The sensor of a digital camera is a light-sensitive charge-coupled device (CCD) or complementary metal-oxide-semiconductor (CMOS). To obtain a color image, three CCDs or CMOSs ​​are required to acquire R, G, and B three basic color components. The price of the sensor is relatively expensive, in order to reduce the volume of electronic products, reduce the cost and complexity, usually only use a single sensor and cover the color filter array on its surface to obtain three basic color components at the same time. Therefore, each pixel of the sensor array can only collect one color component. In order to obtain a full-color image, each pixel...

AI Technical Summary

Problems solved by technology

So far, support vector machine integration has been successfully applied in chaotic time series prediction, intrusion detection, fault diagnosis, radar target high-resolution one-dimensional range image recognition, high-resolution remote sensing image classification, satellite image segmentation and other fields, and shows advantages, but the research on applying it to image demosaicing is still quite rare

Images