Signal-and-Image-Processing

All techniques implemented

1. Erosion: Removes pixels at object edges, useful for thinning object boundaries.
2. Dilation: Adds pixels at object edges, helpful for filling gaps or thickening object boundaries.
3. Opening: Erosion followed by dilation, removes small objects or noise while preserving larger object shapes.
4. Closing: Dilation followed by erosion, fills small gaps or connects objects separated by narrow spaces.
5. Thinning: Reduces object thickness while maintaining topology, suitable for extracting object centerlines.
6. Thickening: Increases object thickness, useful for noise robustness or connecting nearby objects.
7. Hole Filling: Using dilation and erosion in sequence to fill gaps or holes within objects.
8. Boundary Extraction: Identifies and extracts object boundaries, essential for isolating regions or detecting changes in images.
9. Edge Detection: Using convolution techniques to detect vertical, horizontal, diagonal edges on images with and without in-built functions.
10. Video Processing Fundamentals: Converting video to image frames and then applying techniques like blurring, sharpening, thresholding, contrast stretching, increasing or decreasing intensity.
11. Discrete Fourier Transform (DFT): It is a fundamental mathematical operation that allows us to transform a time-domain signal into its frequency-domain representation. To speed up the DFT computation, an algorithm called FFT (Fast Fourier Transform) is used.
12. Inverse Discrete Fourier Transform: The inverse discrete Fourier transform (IDFT) is a mathematical operation that is used to convert a digital signal represented in the frequency domain into the time domain.
13. Upsampling: Upscaling an image is the process of enlarging it without any loss in image quality.
14. Downsampling: Downsampling is the reduction in spatial resolution while keeping the same two-dimensional (2D) representation.