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✴ Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing. It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and signal distortion during processing. Since images are defined over two dimensions (perhaps more) digital image processing may be modeled in the form of multidimensional systems.✴
► This App gives you the knowledge of widely used methods and procedures for interpreting digital images for image enhancement and restoration and performing operations on images such as (blurring , zooming , sharpening , edge detection , e.t.c). It also focuses on the understanding of how the human vision works. How do human eye visualize so many things , and how do brain interpret those images? The App also covers some of the important concepts of signals and systems such as (Sampling , Quantization , Convolution , Frequency domain analysis e.t.c).✦
【Topics Covered in this App are Listed Below】
⇢ Digital Image Processing Introduction
⇢ Signals and Systems Introduction
⇢ History of Photography
⇢ Concept of Dimensions
⇢ Image Formation on Camera
⇢ Camera Mechanism
⇢ Concept of Pixel
⇢ Perspective Transformation
⇢ Concept of Bits Per Pixel
⇢ Types of Images
⇢ Color Codes Conversion
⇢ Grayscale to RGB Conversion
⇢ Concept of Sampling
⇢ Pixel Resolution
⇢ Concept of Zooming
⇢ Zooming Methods
⇢ Spatial Resolution
⇢ Pixels, Dots and Lines Per Inch
⇢ Gray Level Resolution
⇢ Concept of Quantization
⇢ ISO preference curves
⇢ Concept of Dithering
⇢ Histograms Introduction
⇢ Brightness and Contrast
⇢ Image Transformations
⇢ Histogram Sliding
⇢ Histogram stretching
⇢ Introduction to Probability
⇢ Histogram Equalization
⇢ Gray Level Transformation
⇢ Concept of Convolution
⇢ Concept of Mask
⇢ Concept of Blurring
⇢ Concept of Edge Detection
⇢ Prewitt Operator
⇢ Sobel Operator
⇢ Robinson Compass Mask
⇢ Krisch Compass Mask
⇢ Laplacian Operator
⇢ Introduction to Frequency domain
⇢ Fourier Series and Transform
⇢ Convolution Theorem
⇢ High Pass vs Low Pass Filters
⇢ Introduction to Color Spaces
⇢ Introduction to JPEG Compression
⇢ Optical Character Recognition
⇢ Computer Vision and Computer Graphics
Image Formation
⇢ Inside the Camera – Sensitivity
⇢ Digital image Fomation
⇢ Sensitivity and Color
⇢ Idealized Sampling
⇢ Quantization to P levels
⇢ (R,G,B) Parameterization of Full Color Images
⇢ Images as Matrices
⇢ Processing Simple – Transpose
⇢ Simple Processing – Cropping
⇢ Point Processing
⇢ Calculating the Histogram of Point Processed Images
⇢ Piecewise Linear, “Continuous” Point Functions
⇢ More Range Stretching/Compression
⇢ Dynamic Range, Visibility and Contrast Enhancement
⇢ Brief Note on Image Segmentation
⇢ Histogram Equalization
Calculating the Mean and Variance
⇢ Discrete Amplitude Random Variables
⇢ Histogram as a Probability Mass Function
⇢ Histogram Equalization
⇢ Histogram Matching – Specification
⇢ Quantization
⇢ Quantization Artifacts - False Contours
⇢ Designing Good Quantizers
⇢ Companding
⇢ Designing the Reproduction Levels for Given Thresholds
⇢ MSQE Optimal Lloyd-Max Quantizer
⇢ Systems
⇢ Linear Shift Invariant (LSI) Systems
⇢ Convolution
⇢ LSI Systems and Convolution
⇢ The Fourier Transform of 2-D Sequences
⇢ Real-Complex Parts and Symmetry
⇢ Shifting and Modulation
⇢ Delta Functions
⇢ Fourier Transform Types
⇢ Sampling and Aliasing
⇢ Fourier Transform of Sampled Sequence
⇢ Aliasing
