Learn Image Processing like Engineers do ! | Online Course

Explore how quantization maps continuous signals to a smaller set of output values, using sampling and an analog-to-digital converter to transform voltages into discrete binary sequences.

Explore array indexing in digital image processing by building a MATLAB script that draws a letter X using diagonal and flipped diagonal patterns.

Undertake project 1 to create an image of the capital letter A using matrix concepts, with code, hints, and a hands-on exploration of inverse matrices and indexing.

Design and implement the wowface emoji using analytic equations to render shapes, explore grayscale intensities from 0 to 1 and color schemes, with hints and sample code to guide project.

Explore histogram processing in digital image processing by analyzing gray intensity levels, using 256 bins, MATLAB imhist, and normalization to 0–1 for probability-based analysis.

Explore image enhancement with histogram equalization in MATLAB using the histeq function to map intensities to 256 levels, boosting contrast and dynamic range.

Explore neighborhood processing and spatial filtering using masks to apply linear or nonlinear operations to each pixel, and compare correlation with convolution while applying Matlab imfilter to create glow effects.

Demonstrate how spatial filtering uses a one-dimensional mask with zero padding, slides across an input to form full and same correlations, and contrasts correlation with 180-degree rotated convolution.

Learn to blur license plate images with the colfilt filter by converting RGB to grayscale, tuning blur direction and intensity, and finalizing output types for privacy protection.

Explore how frequency filtering enables image enhancement and compression using the 2D discrete Fourier transform. Understand the Fourier spectrum, modulus, phase, inverse transform, and conjugate symmetry for real images.

Code a one-dimensional discrete Fourier transform in Matlab from scratch as an educational exercise, using isvector, numel, and for loops, noting its slow performance compared with fft.

Implement the paddedsize function to compute image padding using AB and CD, with a power-of-two parameter to accelerate the fast Fourier transform.

Explore how low pass filters, including Gaussian, ideal, and Butterworth types, use distances from the center and cutoff frequency D0 to attenuate high frequencies in digital image processing.

Deploy high pass filters to sharpen images by attenuating low frequencies while preserving high frequencies above a cutoff frequency D0, contrasting with low pass filtering that blurs.

Explore high frequency emphasis in image processing by applying high-pass filters, offset, and a multiplier to compensate loss of gray tonality, demonstrated in Matlab.

Explore bandpass and bandreject filters in image processing, built from matching low-pass and high-pass filters; learn their frequency-domain regions, transfer functions, and Butterworth and ideal examples.

Investigate hybrid images by combining two photos using high pass and low pass frequency filters, illustrated through the Albert Einstein and Marilyn Monroe anecdote.

learn how geometric transformations map input pixel coordinates to output coordinates and form affine transformations using matrix a and vector b, combining translation with scaling, rotation, and reflection.

Compare random neighborhood sampling and mean-based interpolation for image shrinking, using a 4x4 matrix and 2x2 blocks to produce a half-size result.

Apply image rotation using a rotate image function by a given degree, convert color to grayscale, and double the input image size for center rotation while handling errors with try-catch.