Open AccessVariance Reduction in Low Light Image Enhancement Model
Author(s) -
V.deepika,
C.S. Nivedha,
P.S. Sai roshini,
Guide S. Arun Kumar
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d4723.119420
Subject(s) - pipeline (software) , computer science , artificial intelligence , computer vision , reduction (mathematics) , process (computing) , image (mathematics) , artificial neural network , contrast enhancement , image enhancement , contrast (vision) , variance (accounting) , pattern recognition (psychology) , mathematics , medicine , geometry , accounting , magnetic resonance imaging , business , radiology , programming language , operating system
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.