Variance Reduction in Low Light Image Enhancement Model
release_moabaes77vdgpj4orcuyjj2i64
2020 p139-142
Abstract
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 [1], a hidden layer is added to the model
which results in a decrease in irregularity.
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