Underexposure image recovery method based on deep learning

Abstract

The invention discloses an underexposure image recovery method based on deep learning, and belongs to the field of image quality enhancement. The problems of low shooting contrast, low brightness, much noise, high complexity of a mainstream image recovery algorithm and high calculation cost caused by uncomfortable light source in the shooting process are solved. The method mainly comprises the following steps: firstly, the input of a multi-scale decomposition network for shooting images with different illuminance is a low-illuminance image, and a series of feature maps smaller than the original image are obtained through down-sampling convolution; secondly, the reflectivity and illuminance of images with different resolutions are obtained at an up-sampling decoding end through decomposition, on the basis of the decomposition, image brightness enhancement is carried out by using a multi-scale brightness adjustment network oriented to image illumination imbalance, and then reconstructionand recovery are carried out by using an image reconstruction network; finally, the reflectivity image and the enhanced illuminance image are integrated to obtain a final restored illumination image.

Description

technical field [0001] The invention belongs to the field of image quality enhancement, and in particular relates to a method for restoring brightness of an underexposed image based on deep learning. Background technique [0002] In real life scenarios, different shooting environments will cause many brightness problems in the captured images. When taking professional photography, the light source is between the photographer and the subject. Due to the good lighting, good shooting effects can be achieved. However, in many cases, the position of the light source is uncontrollable, which often leads to the "big black face" situation in the captured portraits. The underexposed areas in this kind of photos can hardly see the details, and the visual experience is often poor. The further processing of the image also poses great challenges. Professional photographers often use light sources such as reflectors and flashes to increase illumination, but artificial light sources can e...

AI Technical Summary

Problems solved by technology

Among them, the grayscale stretching transformation can adjust the range of the overall grayscale value of the image, and then highlight the target area of ​​the image, but there is a problem with this method: the adjusted image is prone to whitening areas, and the detailed information of the area is lost.

The early method based on Retinex theory, single-scale Retinex (SSR) constrains the illumination map to be smooth through a Gaussian filter and then processes it. This method can well preserve the boundary details of the image, but due to the Gaussian function selected in the SSR algorithm Features, for the two requirements of large dynamic range compression and contrast enhancement, the enhanced image cannot guarantee

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