Improve Image Quality Deep Learning
                     Improve Image Quality 
Deep Learning
Deep learning is a powerful technique for 
extracting meaning from images, and it's 
getting better every day. In this example 
you'll see how to use the open source Caffe 
deep learning framework to improve your 
image quality.
Image quality is one of the most important 
factors in our daily life. Whether it's looking 
at pictures, or watching movies, or playing 
games, image quality is always a big 
concern for users. But improving image 
quality is not an easy task. It requires a lot 
of efforts in many aspects.
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Improve Image Quality Deep Learning
In the past years, researchers have paid 
much attention to improve the image 
quality by employing various techniques 
from different fields such as computer 
vision, machine learning and optimization 
theory. In this post, we will introduce a 
series of methods that use deep learning 
to improve image quality.
Improving image quality with deep learning 
is a tricky area with many different 
approaches, each appropriate for specific 
problems. Some areas of interest include 
image enhancement (e.g. contrast, 
brightness, and color), content-based 
image retrieval, and object recognition.
In a professional tone: In recent years, 
artificial intelligence has become 
increasingly accessible as the necessary 
computing power becomes cheaper and 
better software is developed. Today, even a 
smartphone can be used to process images 
using deep learning algorithms. 
Deep learning is a new name for an old 
concept: artificial neural networks. These 
are inspired by the way the human brain 
works, with many neurons connected 
together in complex, interrelated ways. 
Neurons in the brain each have many 
inputs and outputs, but there is a single 
path of communication between any given 
pair of them. This is similar to the way 
information flows through a deep learning 
algorithm.
 The individual layers of the network are all 
a little different, and they're responsible for 
different things: recognizing an object as a 
whole; extracting basic features like edges 
and curves; or making fine-grained 
distinctions like facial expressions and 
hand gestures. 
The best results are achieved using deep 
learning, which means the computer needs 
to be fed a huge amount of data before it's 
able to recognize objects in pictures. The 
more images your computer is exposed to, 
the more capable it'll be of understanding 
what's in an image and how to recreate it.