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Optical remote sensing image processing performance evaluating method

An optical remote sensing image processing performance technology, applied in image data processing, image enhancement, image analysis, etc., can solve the problems of noise amplification, poor texture detail retention, artifacts near the edge, etc., and achieve effective optimization design Effect

Inactive Publication Date: 2017-07-18
HARBIN INST OF TECH
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Problems solved by technology

At present, there are many studies on optical remote sensing image processing algorithms. These algorithms can significantly improve the image modulation transfer function (MTF) and sharpness, but there are still some problems, mainly reflected in the increase of MTF. Artifacts, noise suppression and poor ability to maintain texture details, etc. Therefore, the improvement of image comprehensive processing for the above restoration quality problems has become one of the important development directions of image restoration processing

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  • Optical remote sensing image processing performance evaluating method

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specific Embodiment approach 1

[0019] Specific implementation mode one: as figure 1 As shown, what is described in this embodiment is a method for evaluating the performance of optical remote sensing image processing, and the steps of the method are as follows:

[0020] Step 1: Establish an objective image evaluation index system for image grayscale, texture and edge information, and evaluate the processing performance of the processing algorithm for detail preservation and definition enhancement;

[0021] Step 2: Combine the modulation transfer function MTF estimation method based on the knife-edge image and the natural image, and use the integral area of ​​the MTF (modulation transfer function) curve to evaluate the overall performance of the processing algorithm for the MTF of each frequency band;

[0022] Step 3: Propose the evaluation index and method of the image signal-to-noise ratio (SNR), and evaluate the comprehensive processing performance of the processing algorithm for detail preservation enhan...

specific Embodiment approach 2

[0024] Specific embodiment two: a kind of optical remote sensing image processing performance evaluation method described in specific embodiment one, the concrete steps of described step one are as follows:

[0025] Evaluate the detail preservation performance and sharpness enhancement performance of the optical remote sensing image processing algorithm according to the gray level, texture and edge information of the image. Research on parameter characterization methods, and obtain image parameters that can characterize texture and edge details. The image parameters mainly include: grayscale contrast, sharpness, spatial frequency, and texture entropy;

[0026] (1) Grayscale contrast

[0027] Gray contrast refers to the ratio or logarithmic difference of the density of the brightest part and the darkest part in the image, which is an important indicator reflecting the contrast between different ground features and the clarity of the image. The more prominent the line, the grea...

specific Embodiment approach 3

[0046] Specific embodiment three: a kind of optical remote sensing image processing performance evaluation method described in specific embodiment one, the concrete steps of described step two are as follows:

[0047] According to the scene features contained in the image, the MTF estimation method based on the knife-edge image or the MTF estimation method based on the natural scene is used to extract the MTF of the image, and then evaluate the MTF improvement performance of the optical remote sensing image processing algorithm; when the scene of the original image contains the knife-edge image For (blade edge) features, choose the MTF estimation method based on the knife-edge image to extract the area of ​​the MTF curve; otherwise, use the on-orbit system MTF estimation algorithm based on natural scenes to extract;

[0048] (1) MTF estimation method based on knife edge image

[0049] First, find the knife-edge point in the given image, fit the knife-edge curve, resample accor...

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Abstract

An optical remote sensing image processing performance evaluating method belongs to the optical remote sensing image processing and evaluating technical field; the method comprises the following steps: building an image objective evaluate index system for image gray scale, texture and edge information, and evaluating the processing performance of a processing algorithm on detail maintenance and sharpness enhancement; combining with a modulation transfer function (MTF) estimate method based on an edge image and a natural image, and using the MTF curve integration area to evaluate the whole lifting performance of the processing algorithm on each frequency band MTF; proposing an image signal to noise ratio (SNR) evaluate index and method, and evaluating the integral processing performance of the processing algorithm on detail maintenance enhancement and noise inhibition; providing a ringing and aliasing effect evaluate method, and evaluating the processing performance of the processing algorithm on pseudo image inhibition. The advantages are that the method is suitable for comprehensively and objectively evaluating the image restoration algorithmic performance advantages and disadvantages, thus guiding processing algorithm parameter optimization and model improvement.

Description

technical field [0001] The invention belongs to the technical field of optical remote sensing image processing and evaluation, and in particular relates to an optical remote sensing image processing performance evaluation method. Background technique [0002] For the optical remote sensing imaging link, due to the influence of atmospheric environment, camera, platform vibration and other factors, remote sensing images have image quality degradation such as blur and noise. Image restoration processing technology is usually used to improve the quality of on-orbit imaging and reduce the difficulty of system development. At present, there are many studies on optical remote sensing image processing algorithms. These algorithms can significantly improve the image modulation transfer function (MTF) and sharpness, but there are still some problems, mainly reflected in the increase of MTF. Artifacts, noise suppression and poor ability to maintain texture details, etc. Therefore, the ...

Claims

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Application Information

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IPC IPC(8): G06T7/00
CPCG06T7/0002G06T2207/10032G06T2207/30168
Inventor 智喜洋江世凯张伟胡建明李鸿飞
Owner HARBIN INST OF TECH
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