The methods are of low computational cost

The last several decades, there have been massive improvements
in modern digital cameras including resolutions and sensitivity. The quality of
videos is still limited. Firstly, videos have poor dynamic range. To capture
images of high dynamic range, most consumer cameras often rely on automatic
exposure control, but longer exposure time results motion blur. Secondly, the image
sequences taken with very low signal-to-noise ratio. The input signal may be
modified by increasing sensitivity of cameras. Various approaches are developed
for enhancing video proposed a noise-adaptive spatiotemporal filter that
considers both Poisson noise and false color noise of input videos. Because
their method aims only to videos slightly lower than normal lighting
conditions, enhancement of input dynamic range is omitted. The developed an
enhancement framework for low dynamic range video based on a virtual exposure
camera model. Their method includes the bilateral ASTA-filter (Adaptive Spatio-Temporal
Accumulation) and tone-mapping with a logarithmic function applied to a large
scale and detail features separately.

Because of camera
resolution and other factors many problems occurs such as color enhancement,
sharpening, pixel quality, visual effect and especially zooming. Super-
Resolution is a method to upscale video and images, i.e. increase resolution of
a video or picture (terms “upsize”, “up-convert” and
“uprez” are also widely used). To up-convert each frame, information
from this very frame and from
a number of neighbor frames is effectively used. If picture in video is not
changing too fast, then information from several frames is added to create a larger
and more detailed picture. There were many video SR algorithms
proposed, it is still
very difficult to handle all kinds of situations. SR techniques can be generally
classified into two categories single-image based and multi-frame based. Single-image based SR mainly interpolation based and example based methods.
Interpolation based methods are
of low computational cost but very limited restoration performance. High-resolution
videos add heavy storage and network
transmission burdens to the current video systems. In order to promote visual experience
sufficiently on high-resolution
display devices, video super-resolution (SR) is particularly essential. Video super-resolution is class of technique
that enhances the resolution of video system. Video SR techniques can also help video coding and decoding, face video
hallucination, video surveillance
systems, remote sensing systems,
intelligent robotic system, object recognition system, medical image analysis and
stereoscopic video processing.
quality in general plays a vital role in many fields like engineering, social
areas as well as in medical Researchers and companies are now exploring efficient methods for accurate
video SR. The resolution is increase of video with motion based Super-Resolution method, where
each frame is upsized using information from a number of neighbor frames to
extract maximum details for outstanding results. The convert low resolution
video to high resolution of flexible video editing like de-interlacing,
de-noising, de-blocking, color correction, stabilizing, sharpening, visual
effects, zooming etc.

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In proposed work SR reconstruction applied
specially for zooming portions. In this video quality when video is zoom it
will give accurate video zooming instead of blurring pixel at a time of zooming
video. Zooming is simply means enlarging a picture in a
sense that the details in the image became more visible and clear. There are
two different steps of zooming. The first step includes zooming before taking
an particular image. This is known as pre-processing zoom. This zoom involves
hardware and mechanical movement. The second step is to zoom once an image has
been captured. It is done with many different algorithms in which we manipulate
pixels to zoom in the required portion. An image is zooming means changing the
number of display pixels per image pixel only in appearance. At zoom=1, there
is one display pixel per image pixel. At zoom=2, there are 2 display pixel per
image pixel in both x and y this enlargement is quantified by a calculated number
or number is