NUST Institutions Library Catalogue

A number of computer vision systems and applications require high resolution, visually
enhanced images with high contrast and preserved color and detail information, as inputs.
However, in reality, due to the camera sensor limitations and challenging and adverse imaging
conditions, such as poor lighting or bad weather, the captured images may suffer from
low contrast, reduced visibility, haze, distorted colors or low resolution. Therefore the images
need to be enhanced before they can be used for various computer vision systems.
Image enhancement techniques aim to improve the visual appearance of images, and make
them suitable for human/machine perception, so that they can be used in their required image
processing and computer vision applications, such as surveillance and security systems,
target identification, scene analysis, medical image processing, satellite imagery and remote
sensing.
This thesis presents various image enhancement techniques from the perspective of resolution
enhancement using super resolution and visibility enhancement using image dehazing.
In this regard, five different image enhancement techniques focusing on resolution and visibility
enhancement are presented.
The first technique focuses on image resolution enhancement, in which compressive sensing
through sparse representation, based on self example dictionary learning and guided filtering
is used for super resolution of images. The effectiveness of the proposed methodology is
verified through quantitative and visual analysis. The last four techniques target visibility enhancement
of different types of hazy images including outdoor, underwater, satellite/aerial
and low light images through various dehazing methodologies. The second technique is
based on filtering, detail enhancement and contrast improvement for the visibility enhancement
of underwater images with poor visibility. Visibility enhancement and dehazing of
images using local Laplacian filtering and l0 gradient decomposition is proposed as the third
enhancement technique. The fourth image visibility enhancement technique uses image decomposition,
detail enhancement and fusion for dehazing of images. The fifth technique for
visibility enhancement makes use of edge preserving image decomposition and application
of different enhancement strategies on the basis of whether the image is a dark low light
image, or a hazy image. The technique works well for low light, as well as underwater and
outdoor hazy images. The presented techniques generate effectively dehazed, visually plausible
images, with enhanced visibility, improved contrast and preserved image details.
Visual and quantitative comparison of the presented techniques with existing state of the art techniques demonstrates the effectiveness of the proposed image enhancement methodologies.