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This image is displayed without any further enhancement.

The contrast between different features has been improved.

Different landcover types in an image can be discriminated usingsome image classification algorithms using spectral features, i.e.

The classification procedures can be "supervised" or"unsupervised". In supervised classification, the spectral features of some areas of known landcover types are extracted from the image.

The image may also be transformed to conform to a specific map projection system.

The amount of details depend on the image resolution.

The image has also been transformed to conform to a certain map projection (UTM projection).

A photograph captured by a digital camera may be the final product for
many casual photographers. However, for professional photographers,
this photograph is only the beginning: experts often spend hours on
enhancing and stylizing their photographs. These enhancements range
from basic exposure and contrast adjustments to dramatic alterations.
It is these enhancements - along with composition and timing - that
distinguish the work of professionals and casual photographers.
The goal of this thesis is to narrow the gap between casual and
professional photographers. We aim to empower casual users with
methods for making their photographs look better. Professional
photographers could also benefit from our findings: our enhancement
methods produce a better starting point for professional processing.
We propose and evaluate three different methods for image enhancement
and stylization. First method is based on photographic intuition and
is fully automatic. The second method relies on expert's input for
training; after the training this method can be used to automatically
predict expert adjustments for previously unseen photographs. The
third method uses a grammar-based representation to sample the space
of image filter and relies on user input to select novel and
interesting filters.

Thesis Supervisor:
Prof. Fredo Durand

Given a depth image and a color image of the same resolution, we can utilize the color image as a guide to improve the accuracy of the depth image, by smoothing out edges and removing holes as much as possible.

Matlab Image Enhancement Techniques - SlideShare

I work in computer vision, the area of computerscience concerned with automatically inferring semantic meaning fromimages -- teaching computers to "see." More generally, I am interestedin problems that involve analyzing and modeling large amounts ofuncertain data, like mining data from social networking websites.

It is useful to examine the image Histograms before performing any image enhancement.

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Matlab Image Enhancement Techniques 1


Matlab:Image enhancement techniques<br /> 2

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Color image with Dim regions Enhancement Using …

(1994) Multi-resolution image processing and learning for texture recognition and image enhancement. Dissertation (Ph.D.), California Institute of Technology.

How can events contribute to destination image enhancement

A general recognition framework is presented that consists of multi-resolution pyramidal feature-extraction and learning paradigms for classification. The system is presented in the context of the texture recognition task.

In the feature extraction part of the system, an oriented Laplacian pyramid is used as an efficient filtering scheme to transform the input image to a more robust representation in the frequency and orientation space. An optimal technique is presented for computing a steerable representation of the pyramid. Steerability is used to generate a rotation-invariant input representation.

In the learning stage of the system we focus on a rule-based probabilistic learning scheme. This information-theoretic technique is utilized to find the most informative correlations between the attributes and the output classes while producing probability estimates for the outputs. Both unsupervised and supervised learning are utilized. Apart from the rule-based approach we experiment with other non-parametric classifiers, such as the k-nearest neighbor classifier and the Backprop neural-network.

We demonstrate experimentally that our scheme improves significantly upon the state-of-the-art both in rotation-invariant classification and in orientation estimation. A variety of applications are presented, including autonomous navigation scenarios and remote-sensing, as possible extensions for the texture recognition system. A generalization of the system to face-recognition is discussed.

In the latter part of the thesis, a procedure for creating images with higher resolution than the sampling rate would allow is described. The enhancement algorithm augments the frequency content of the image by using a non-linearity that generates phase-coherent higher harmonics. The procedure utilizes the Laplacian pyramid image representation. Results are presented depicting the power-spectra augmentation and the visual enhancement of several images. Simplicity of computations and ease of implementation allow for real-time applications such as high-definition television (HDTV). An initial investigation is pursued to combine the enhancement scheme with pyramid coding schemes.

Thesis on image enhancement by Rhonda Strickland - …

In this thesis, we show the application of guided filters to the depth refinement problem, utilize a guided inpainting model to fill in any holes that may arise in the depth image, as well as extend the filter out to the temporal domain to handle temporal flickering.

Thesis on image enhancement techniques September 5, …

These procedures include radiometric correction to correct for uneven sensor response over the whole image and geometric correction to correct for geometric distortion due to Earth's rotation and other imaging conditions (such as oblique viewing).

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