Visual acquisition is a necessary condition in image processing.However

under the low-illumination conditions of nighttime

images obtained using a visual system lose a considerable number of effective features

and thus

always appear to have low contrast and brightness.This scenario will negatively affect the subsequent processing of computer vision applications

such as intelligent surveillance

object detection

and pedestrian tracking.Image enhancement is generally regarded as an effective method for improving visual effects.This method augments valid information by increasing the difference among features and enhancing regions of interest.Existing image enhancement methods include direct and indirect enhancement algorithms.The concept of indirect enhancement algorithms originated from Dong

who found that a low-light image would be similar to a fog image after inversion.Accordingly

low-illumination image enhancement optimization can be extended to fog image restoration.Fog removal algorithms have been introduced to execute an enhancement-like process.However

several enhancement algorithms for low-illumination images are sensitive to noise and easily saturated.This study proposes a low-illumination indirect image enhancement algorithm to improve image quality and avoid the aforementioned imperfections. Retinex theory is based on color constancy.An image is segmented into two parts in the division operation.One part is an entrance map

whereas the other is a reflection map.Subsequently

the original reflection component is obtained by reducing

or even eliminating

the impact of the entrance map on the reflected map.Multi-scale retinex with color restoration (MSRCR) is an optimized solution for the color distortion caused by retinex.This solution is based on multi-scale retinex.The introduction of the color recovery factor is a key point

which adjusts the proportional relationship of each RGB component from the original image.However

the He-based method is introduced.The atmospheric scattering model is a fundamental structure of the He-based method.Fog-relevant statistics

namely

dark-channel prior

is obtained by observing fog images.Atmospheric light is acquired according to the obtained information.Coarse transmissivity is estimated by using a minimum filter

and the soft matting interpolation method is applied for optimization.Similar to the He-based method

our method is based on a fog-degraded model.First

our method inverses a low-illumination image and then it generates a pseudo-fog image.The characteristics of the pseudo-fog image differ from those of a foggy image.The pseudo-fog image is mainly characterized by a large occupied bright area and a high atmospheric light value.The classic dark channel theory on fog images is inapplicable to large occupied bright regions and will lead to imprecise transmission map estimation.Second

our method trains a specific convolutional neural network (CNN) to directly predict optical transmissivity for the input image.Synthetic pseudo-fog patches are selected as input

pseudo-fog features are acquired via the progressive convolutional operations of each layer

and Brelu activation function outputs a corresponding transmission parameter ranging from 0 to 1.Third

our method obtains an atmospheric light map replaced with a local atmospheric light value to address the oversaturation caused by global atmospheric light.Guided filtering is adopted to refine the optical transmissivity map and atmospheric light map.The gray scale of the pseudo-fog image is used as a guide image to optimize the transmission map obtained by CNN and the atmospheric light map.The final enhanced image is a reverse result of a fog-removed image

which is restored based on the atmospheric scattering model. This study designs three sets of controlled experiments

which are performed on classical image enhancement algorithms

namely

retinex

MSRCR

and indirect enhancement algorithms

including the He-based algorithm.The first set of experiment is conducted from the perspective of subjective visual effects.Accordingly

the second set is evaluated from objective experiments under several evaluation indices

including average gradient

information entropy

and peak signal-to-noise ratio (PSNR).The last experiment is proposed to verify the effectiveness of the parameters (i.e.

transmissivity and atmospheric light) obtained using the proposed method.The result of the subjective and objective aspects shows that retinex is sensitive to noise and causes color distortion

MSRCR tends to cause overexposure due to an excessive increase in mean value

the He-based algorithm according to dark channel prior theory is liable to produce saturation because of its inapplicability to pseudo-fog images

and our method effectively improves visual effects.Our method does not only effectively improve the brightness of low-illumination images

but also avoid evident color distortion and overexposure of restored images.The performance on the PSNR index indicates that compared with a sub-optimal method

our algorithm increases by an average of 2.6. The CNN is a widely used feature extraction method.We adopt this method to learn pseudo-fog features via large-scale training of synthetic data

replace the global atmospheric light map with a local one to avoid saturation

and perform guided filtering to refine this map.The results of the qualitative and quantitative experiments show that the proposed algorithm can improve the visual effects and strong adaptability of a scene and can enhance indoor and outdoor low-light images.If combined with CUDA technology

then tour method can be used for the real-time enhancement of videos.