Fused image quality integrated evaluating method based on fuzzy neural network

Abstract

The invention pertains to the field of the image fusion technology in image process, which relates to a quality comprehensive evaluation method of fusion images based on fuzzy neural network and comprises the following steps: a sample set of fusion images is established, and each group of samples comprises a subjective evaluation grade sample of fusion images and two or more than two objective evaluating indicator samples obtained by evaluating the fusion image objectively; a quality evaluation module of fusion images based on fuzzy neural network is established; the obtained samples are trained, and the subjective evaluation grade sample of fusion images is adopted as expected output, and the correlation parameters for evaluating indicator weighing and fuzzy membership function are generated through network learning; the objective evaluating indicator of fusion images to be evaluated is calculated, and the evaluation grade result is generated by taking advantage of the established fusion image quality evaluation module. The method of the invention has comparatively good flexibility, and in the way of network training, novel fusion image quality evaluating indicator is learnt, so as to expand network evaluation ability and realize completely automatic evaluation.

Description

technical field [0001] The invention relates to the technical field of image fusion in image processing, and relates to a fusion image quality evaluation method. Background technique [0002] As an emerging discipline, image fusion technology has been widely used in military, medical, remote sensing and many other fields due to its outstanding detection advantages, and the image fusion algorithm has also entered a relatively mature stage. However, compared with the maturity of the fusion algorithm itself, there are still great limitations in the evaluation of the fusion image quality. [0003] Currently, the evaluation of fused images includes subjective evaluation and objective evaluation. The former relies on the subjective feeling of the observer, but the conclusion of the evaluation will vary with the interest of the observer and the requirements of the application field and occasion; the latter is often judged according to some calculable indicators without human parti...

AI Technical Summary

Problems solved by technology

The former relies on the subjective feeling of the observer, but the conclusion of the evaluation will vary with the interest of the observer and the requirements of the application field and occasion; There are many, such as entropy, cross entropy, mutual information, average gradient, spatial frequency, etc. Each index can only reflect a certain aspect of image fusion, and it is difficult to achieve comprehensive evaluation

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