A Method of Image Super-resolution Enlargement Based on Edge Classification

Abstract

The invention discloses an image enlargement method based on edge classification. The method first performs edge detection on the low-resolution image to obtain a binarized image indicated by the edge of the low-resolution image. Then, an initial interpolation is performed on the low-resolution image to obtain an initial high-resolution image. Next, extract a 3×3 image block in the high-resolution image, classify the image block according to the direction of the edge in the binarized image corresponding to this block, and classify some pixels in the block according to its category Re-interpolate. The method of the present invention well considers the characteristics of the direction of the edge, and performs interpolation according to this, so that the details of the enlarged image are relatively clear, especially the edge part and the part close to the edge. Experiments show that the reconstructed image is very close to the original high-resolution image.

Description

technical field [0001] The invention relates to a new method for super-resolution image enlargement in image processing. Especially according to the edge information on the low-resolution image, a small image block on the initially enlarged high-resolution image is classified according to the edge points on the corresponding low-resolution image, and a certain image block in the image block is classified according to this classification. The method of re-interpolating some pixels. Background technique [0002] With the rapid development of digital cameras and Internet technology, people's demand for high-definition digital images is increasing day by day. However, due to the limitation of network bandwidth and storage space, the cost of storage and transmission of high-definition images is relatively high, which consumes system resources, such as consuming a very large storage space or a very large bandwidth of the system. For this reason, digital image compression technol...

AI Technical Summary

Problems solved by technology

Among them, the example-based method needs to first establish a sample database composed of low-resolution image blocks and their corresponding high-resolution image blocks obtained by training, and its computational complexity is very large, making it difficult to apply in real time

Based on the interpolation method, although its computational complexity is low, it is easy to blur the image and make the edge of the image unclear.

The human eye is more sensitive to the edge of the image, and its lower distortion will greatly reduce the visual effect of the image.

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