To solve the problems of low contrast and brightness as well as high noise level in low-illumination videos

a fast and effective low-illumination video enhancement algorithm is proposed by combining retinex theory with dark channel prior theory to improve contrast and reduce noise. Considering enhancing low illumination videos and amplifying noise simultaneously

removing noise before enhancing videos is beneficial to improving video enhancement effects. Therefore

this study combines the advantages of guided filtering and median filtering to propose an improved comprehensive denoising algorithm

which is applied to the YCbCr space. Then

the luminance transmission map is estimated by extracting luminance components. Furthermore

the atmospheric model is applied to recover the low-illumination video. Finally

scene detection

edge compensation

and inter-frame compensation are added to further improve the effectiveness and speed of the process. The proposed algorithm can effectively improve the brightness and contrast of low-illumination videos

reduce noise

strengthen the detailed information of videos

and diminish video scintillation

thereby improving the quality of videos. The proposed algorithm has a dominant advantage in processing speed

which is over 10 times faster than Dong's algorithm and the Retinex algorithm. Experimental results show that the proposed algorithm exhibit superior performance over other algorithms. First

the continuity of inter-frame motion can be guaranteed. Second

the enhancement effects and processing speed can be improved. Third

details and edging outlines are processed carefully

which results in unique effects that cannot be achieved by other algorithms. Therefore

the proposed algorithm can be applied in various areas

such as video surveillance

target tracking

and intelligent transportation systems

to achieve real-time video enhancement.