SIFT Feature Extraction for Images Using MATLAB

Implementing SIFT Feature Extraction in MATLAB with Feature Matching Under Varying Illumination and Viewpoints

Super-Resolution

The effectiveness of super-resolution (SR) techniques relies on leveraging slightly different perspectives across multiple low-resolution images containing the same object. This process aggregates complementary information to exceed the data available in any single frame. Optimal performance is achieved when objects exhibit motion in video sequences, enabling motion detection and tracking to multiply benefits through sub-pixel alignment and temporal fusion. If objects remain static across frames, no additional information can be extracted. However, rapid motion or transformation creates distinct appearances across frames, which can be exploited through registration algorithms and motion compensation techniques to reconstruct high-resolution details.

Implementing Inverse Perspective Mapping for Image Transformation

To perform inverse perspective mapping on images, camera height, field of view, and the desired output image's horizontal width (in meters) must be specified. With accurate parameters, high-quality inverse perspective transformed images can be generated. Key implementation involves calculating homography matrices using OpenCV's warpPerspective function with properly configured projection parameters.