TY  - JOUR
T1  - Variance Reduction in Low Light Image Enhancement Model
AU - Arun Kumar, S. AU - Deepika, V. AU - Sai Roshini, P.S. AU - Nivedha, C. 
JO  - Journal of Engineering and Applied Sciences
VL  - 16
IS  - 3
SP  - 114
EP  - 118
PY  - 2021
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2021.114.118
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2021.114.118
KW  - Enhancement
KW  -considered
KW  -simultaneously
KW  -processing
KW  -pipeline
AB  - In image processing, enhancement of images
taken in low light is considered to be a tricky and intricate
process, especially for the images captured at nighttime.
It is because various factors of the image such as contrast,
sharpness and color coordination should be handled
simultaneously and effectively. To reduce the blurs or
noises on the low-light images, many papers have
contributed by proposing different techniques. One such
technique addresses this problem using a pipeline neural
network. Due to some irregularity in the working of
the pipeline neural networks model, a hidden layer is
added to the model which results in a decrease in
irregularity.
ER  -