Ultra-spectrum image real-time compression system based on noise decomposing compression algorithm

Abstract

A hyperspectral image real-time compression system based on a noise decomposition compression algorithm, including a host, a high-speed image slave processor, and an image acquisition device, the image acquisition device is connected to the slave processor through a CAB bus interface and connected to the host through a PCI bus; This main frame becomes two-way connection from processor through PCI bus, and this main frame connects image display and image data compression result memory respectively, it is characterized in that: a. be provided with the image noise decompression compression module that becomes two-way connection on the processor and Host interface, in order to accept command data from the host and transmit image real-time compression result data to the host; b. Host control template is provided on the host, from the processor interface and display module and storage module, the control module accepts the original image data and user commands to form a system data source, and send commands to the slave processor through the slave processor interface and receive image real-time compression result data; the host control module sends the original image data to the display module and sends the image real-time compression result to the storage module to form compression document.

Description

technical field [0001] The present invention relates to a hyperspectral image real-time compression system, in particular to an image compression system based on a noise decomposition compression algorithm, which reduces the influence of noise introduced by the compression algorithm on image information by estimating the signal-to-noise ratio of the real-time hyperspectral image To the minimum, it realizes the high-fidelity compression of the hyperspectral image, and also guarantees the image compression rate of the image compression system. Background technique [0002] There have been many reports on the research results of hyperspectral image compression, but most of them stay in the stage of theoretical research and software processing, and there are not many real-time hardware applications. The reasons are as follows: firstly, the data volume of hyperspectral images is huge, which requires high processing power of hardware, and it was difficult to complete it in real ti...

AI Technical Summary

Problems solved by technology

[0004] However, the actual remote sensing images inevitably have the interference of random noise. The shortcoming of analyzing these imaging spectral image compression systems is that they do not consider the noise characteristics of the image. When the image is compressed by a certain multiple, the image edge The original noise of the transition zone is amplified by the system, and spreads and pollutes the high-bit data of the image, resulting in the loss of image information

In short, when using these compression systems to compress images, the image information cannot be reasonably preserved

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