Backlight image enhancement and denoising method based on foreground-background separation

Abstract

The invention discloses a backlight image enhancement and denoising method based on foreground-background separation. The backlight image enhancement and denoising method comprises the steps that a backlight image is divided into a foreground area and a background area by adopting an interactive cutout algorithm; the pixel points in the foreground area are enhanced by adopting an improved Retinex algorithm; equalization processing is performed on the pixel points in the background area by adopting a CLAHE algorithm; denoising is performed on the foreground area after enhancement and the background area after equalization processing by adopting a multi-scale NLM algorithm; and weighted fusion is performed on the foreground area and the background area after denoising so that an enhanced and denoised backlight image is obtained. Different enhancement and denoising methods are respectively adopted for the foreground area and the background area of the backlight image so that detail enhancement of the foreground area of the backlight image can be realized, the background area can be protected from being excessively enhanced, the denoising effect is great and accuracy is high and thus the backlight image enhancement and denoising method can be widely applied to the field of backlight image processing.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a backlight image enhancement denoising method based on foreground and background separation. Background technique [0002] Glossary: [0003] Retinex algorithm: Retinex is the abbreviation of "Retina" (retina) and "Cortex" (cerebral cortex). The Retine algorithm is an image enhancement algorithm based on the human visual system based on scientific experiments and scientific analysis. It can achieve a balance in dynamic range compression, edge enhancement and color constancy, and adapt to various types of images. sexual enhancement; [0004] CLAHE algorithm: Contrast-limitedAdaptiveHistogramEqualizationalgorithm, limited contrast adaptive histogram equalization algorithm; [0005] NLM algorithm: Non-Local Means algorithm, non-local average image denoising algorithm, estimates the center point of the reference block by weighting the self-similar structural block, thereby reducin...

AI Technical Summary

Problems solved by technology

However, the processing of backlit images is still a difficult problem without a mature solution

Complex lighting conditions greatly increase the difficulty of backlight image processing

Traditional image enhancement methods often cause the details of the foreground area of ​​the backlit image to be enhanced insignificantly, while the background area is over-enhanced. At the same time, the backlit image has different noise levels in the foreground area and background area. Traditional single-scale image noise reduction Doesn't denoise backlit images well either

Backlit images processed by traditional methods cannot accurately enhance and denoise backlit images, which not only affects the visual sense, but also creates certain obstacles to a series of subsequent image processing tasks such as image segmentation, feature extraction, and super-resolution reconstruction.

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