Image real-time super-resolution reconstruction method based on GPU acceleration

Abstract

The invention discloses an image real-time super-resolution reconstruction method based on acceleration of a GPU. The image real-time super-resolution reconstruction method based on the acceleration of the GPU mainly solves the problem that an existing high quality image super-resolution reconstruction serial algorithm is hard to process in real time. The method comprises the following steps of (1) inputting a low-resolution image, (2) carrying out bicubic interpolation up-sampling on the low-resolution image to obtain an interpolation image, (3) carrying out image deconvolution operation based on consistent gradient and opposite direction constraint to the obtained interpolation image, and carrying out parallelization acceleration on the deconvolution operation on the GPU so as to obtain an output super-resolution image in ultrashort time. Experiment shows that the image real-time super-resolution reconstruction method based on the acceleration of the GPU can obtain high quality super-resolution results which contain good details and are clear in texture and natural and reasonable in structure in quite short computing time, and can be widely applied to relative application fields of changing of image resolution.

Description

technical field [0001] The invention belongs to the field of digital image and video processing, and in particular relates to a GPU-accelerated image spatial resolution reconstruction method, which can be used for problems in related application fields of image and video resolution changes, such as medicine, satellite image imaging and target detection and identification. Background technique [0002] Super-resolution (SR) technology is one of the most basic problems in computer vision and digital image processing. It refers to recovering high-resolution images from one or more low-resolution (LR) images. Image process and techniques. The resolution mentioned here not only refers to the physical resolution of the image, but more importantly, it emphasizes the visual quality of the image in terms of details, edges, and clarity. With the improvement of the quality and resolution of display components in equipment such as high-definition televisions and high-end mobile phones...

AI Technical Summary

Problems solved by technology

[0005] Due to the large amount of image data itself, and the super-resolution reconstruction technology often increases the complexity of the algorithm in exchange for high result quality

Due to the long processing time, the existing super-resolution methods based on CPU serial implementation are often difficult to perform real-time processing while obtaining good processing results.

This limits the scope and popularization of the application of super-resolution technology to a certain extent.

A fast and high-quality image, Video resolution reconstruction method, and try to use GPU to accelerate the calculation process, but because it fails to perform reasonable parallelization acceleration on the calculation bottleneck in the serial calculation of the method, the GPU parallel implementation of this method can only be compared to the CPU The serial implementation gets a lower speedup and is far from real-time processing

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