Digital Image Processing

✴ 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