Image Processing using Matlab projects

Image processing is nothing but computer imaging, where its benefits are to the human visuals. Image processing using Matlab projects are used to evaluate and employ Image enhancement and data compression of the image.DIP is not continued on a particular area but has a vast knowledge when analyzed. Image processing using matlab projects include three portions. They are as follows

1. Image Restoration.
2. Image compression.
3. Image enhancement.

Image Restoration:

This method is taking some image with a familiar decline value and again storing  it in its original plane.

Image compression:

In compression technique we extract features like segmentation, image morphology and pattern recognition from the image.

Image enhancement:

To highlight certain characteristics of an image we need to bring out specific features from the image. This technique links taking an image and developing it optically by considering the benefits of man’s visual responses.

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2015 IEEE Image Processing using Matlab projects

• Compressed-Domain Ship Detection on Spaceborne Optical Image Using Deep Neural Network and Extreme Learning Machine.
• Impedance Imaging With First-Order TV Regularization.
• Futuristic Greedy Approach to Sparse Unmixing of Hyperspectral Data.
• A Geometric Matched Filter for Hyperspectral Target Detection and Partial Unmixing.
• Dimension Reduction Using Spatial and Spectral Regularized Local Discriminant Embedding for Hyperspectral Image Classification.
• Spatial-Aware Dictionary Learning for Hyperspectral Image Classification.
• Faraday Rotation Retrieval Using SMOS Radiometric Data.
• Model-Based Fusion of Multi- and Hyperspectral Images Using PCA and Wavelets.
• PET Image Reconstruction Using Kernel Method.
• Landmark Detection for Fusion of Fundus and MRI Toward a Patient-Specific Multimodal Eye Model.
• Stereo Matching with Optimal Local Adaptive Radiometric Compensation.
• Graph-Based Supervised Automatic Target Detection.
• An Adaptive Subpixel Mapping Method Based on MAP Model and Class Determination Strategy for Hyperspectral Remote Sensing Imagery.
• Fall Detection in Homes of Older Adults Using the Microsoft Kinect.
• Gabor Feature-Based Collaborative Representation for Hyperspectral Imagery Classification.
• Supervised Variational Model With Statistical Inference and Its Application in Medical Image Segmentation.
• Multiple Feature Learning for Hyperspectral Image Classification.
• Ultrasound Current Source Density Imaging of the Cardiac Activation Wave Using a Clinical Cardiac Catheter.
• Motion Estimation in Cardiac Fluorescence Imaging With Scale-Space Landmarks and Optical Flow: A Comparative Study.
• Semiparametric Statistical Stripmap Synthetic Aperture Autofocusing.

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Image Registration in Image processing matlab projects is very important in fields like medical imaging and remote sensing. Pixels in a image has many dots representing grayscale or color value which are given to it. The processing toolbox guides image registration based on intensity.

Masking of image is given with a example like a image of road in remote sensor where the area of road without 50m wide from the centre is masked.

In image processing the input taken is always a form of image may be photograph but the output can be of any form may be an image or characteristics or any parameter related to the image.

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