Image enhancement method and system

Abstract

The invention discloses an image enhancement method and system, and the method comprises the steps: carrying out the super-resolution of an input image I, obtaining an image I ', carrying out the stationary wavelet transformation of the image I', and obtaining a low-pass sub-band L; carrying out stationary wavelet transform on the input image I to obtain detail sub-bands Hh, Hv and Hd, then respectively enhancing the detail sub-bands Hh, Hv and Hd, and then carrying out super-resolution; and performing inverse stationary wavelet transform on the low-pass sub-band L and the detail sub-bands Hh,Hv and Hd after super-resolution to obtain an enhanced image. The system comprises a first super-resolution module, a first stationary wavelet transform module, a second stationary wavelet transformmodule, a detail sub-band enhancement module, a second super-resolution module and an inverse stationary wavelet transform module. According to the image enhancement method and system based on the improved FSRCNN, the obtained enhanced image is clear in detail, high in contrast ratio, good in visual effect, excellent in objective evaluation index and good in real-time performance.

Description

technical field [0001] The invention relates to the technical field of image enhancement, in particular to an image enhancement method and system. Background technique [0002] The purpose of image enhancement is to enhance the visual effect of images, so image super-resolution can also be incorporated into generalized image enhancement techniques. Due to the characteristics of infrared imaging technology, infrared images generally have problems of low contrast, blurred edges and unobtrusive details. Therefore, image enhancement technology is needed to improve the quality of infrared images. [0003] At present, there are mainly two types of infrared image enhancement methods applicable in the market, image enhancement methods based on spatial domain and image enhancement methods based on transform domain. [0004] The spatial domain image enhancement algorithm directly processes the pixels in the image, and is fundamentally based on the grayscale mapping transformation of ...

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Problems solved by technology

[0006] In addition, some emerging image enhancement methods have also been proposed and achieved good results, such as multi-scale analysis methods, enhancement methods based on fuzzy theory, enhancement methods based on human visual characteristics, etc. The processing effect of these algorithms is significantly better than Enhancement methods in the space domain and frequency domain, but these algorithms also have certain disadvantages, such as high space complexity and time complexity, and cannot meet the real-time requirements of the system, etc.

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