@article{MAKHILLJEAS2018131116292,
    title = {Quality Measures Technique for Underwater Images Using
Artificial Neural Network},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {13},
    number = {11},
    pages = {3916-3921},
    year = {2018},
    issn = {1816-949x},
    doi = {jeasci.2018.3916.3921},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2018.3916.3921},
    author = {Priya and},
    keywords = {Image enhancement,Underwater Image Sharpness Measure (UISM),Underwater Image Colorfulness Measure (UICM),Underwater Image Contrast Measure (UIConM),Underwater Image Quality Measures (UIQM),Artificial Neural Network (ANN)},
    abstract = {The visibility in underwater images is usually bad because of the attenuation of light in water that
causes the dizzy contrast and the color variation. In this proposed research an innovative and productive
approach for underwater image quality enhancement will have presented. The proposed method intends to
prepare better underwater image contrast, increase image details with fewer losses in information and reduce
noise by applying an innovative procedure of using contrast stretching to produce two different images with
different contrasts. Capturing images through digital camera or smart phones has become simple nowadays.
Because of the enhancement of camera properties, image in underwater and low light conditions is suffered from
exposure problem. The proposed method is combination of different methods which is successful in magnify
the capture in underwater by using the artificial neural network. Underwater image quality measures technique
for underwater images using artificial neural network will have proposed. The underwater image quality
measures technique is proposed for the image quality improvement on the basis of three different techniques.
For the recognition of suitable pixel sets from the underwater image we use image colorfulness, sharpness and
contrast based pixels and train a proposed system to classify that pixel set by using Artificial Neural Network
(ANN). At last performance metrics of proposed underwater image quality measures is calculated and compared
with previous existing research.}
    }