2 research outputs found
Multiple Linear Regression Haze-removal Model Based on Dark Channel Prior
Dark Channel Prior (DCP) is a widely recognized traditional dehazing
algorithm. However, it may fail in bright region and the brightness of the
restored image is darker than hazy image. In this paper, we propose an
effective method to optimize DCP. We build a multiple linear regression
haze-removal model based on DCP atmospheric scattering model and train this
model with RESIDE dataset, which aims to reduce the unexpected errors caused by
the rough estimations of transmission map t(x) and atmospheric light A. The
RESIDE dataset provides enough synthetic hazy images and their corresponding
groundtruth images to train and test. We compare the performances of different
dehazing algorithms in terms of two important full-reference metrics, the
peak-signal-to-noise ratio (PSNR) as well as the structural similarity index
measure (SSIM). The experiment results show that our model gets highest SSIM
value and its PSNR value is also higher than most of state-of-the-art dehazing
algorithms. Our results also overcome the weakness of DCP on real-world hazy
imagesComment: IEEE CPS (CSCI 2018 Int'l Conference
English Out-of-Vocabulary Lexical Evaluation Task
Unlike previous unknown nouns tagging task, this is the first attempt to
focus on out-of-vocabulary (OOV) lexical evaluation tasks that do not require
any prior knowledge. The OOV words are words that only appear in test samples.
The goal of tasks is to provide solutions for OOV lexical classification and
prediction. The tasks require annotators to conclude the attributes of the OOV
words based on their related contexts. Then, we utilize unsupervised word
embedding methods such as Word2Vec and Word2GM to perform the baseline
experiments on the categorical classification task and OOV words attribute
prediction tasks