Developing Image Processing Software Using MATLAB or VC Languages

Developing image processing software using MATLAB or VC++ languages with the following functionalities:

I. Experimental Modules

1. Image reading and display for both grayscale and color images, with color-to-grayscale conversion capability (8 credit hours). Implementation tip: Use imread() for loading images and rgb2gray() for color conversion in MATLAB, while VC++ would require GDI+ or OpenCV libraries for similar operations.

2. Image resolution reduction and enhancement functionality (16 credit hours). Code approach: Implement downsampling using interpolation methods (nearest-neighbor, bilinear) and upsampling with reconstruction filters. MATLAB's imresize() or custom VC++ algorithms using convolution operations.

3. Grayscale histogram calculation and visualization, with analysis of histogram distribution characteristics across different images (16 credit hours). Technical detail: Use imhist() in MATLAB or develop histogram calculation algorithm in VC++ by iterating through pixel values and creating frequency distribution arrays.

4. Histogram equalization processing for the above images, with analysis of processing results (16 credit hours). Algorithm explanation: Implement histogram equalization using cumulative distribution function transformation to enhance contrast. Key function: histeq() in MATLAB or custom mapping function in VC++.

5. For given color images: display noise images of R/G/B components and perform median/mean filtering on H/S/I components with template sizes to be specified (16 credit hours). Implementation: Color space conversion using rgb2hsv(), noise addition with randn(), and filtering with medfilt2()/imfilter() in MATLAB. VC++ would require color space conversion algorithms and filtering implementations.

6. Functional interface design for the above features (8 credit hours)

II. Design Modules

1. Noise analysis: Capture images under different lighting conditions, extract background grayscale regions (uniform灰度), perform histogram statistics on sub-images, and analyze noise intensity under varying illumination. Technical approach: Implement region selection tools and statistical variance calculations for noise quantification.

2. Design Gaussian smooth filter and weighted smooth filter, performing image smoothing with both filters using template sizes to be specified, analyzing processing results and discussing outcomes with different template sizes. Algorithm detail: Gaussian filter implementation using 2D Gaussian kernel convolution, weighted filter using custom weight matrices based on pixel distances.

3. Functional interface design for the above features.

4. New feature: Add image rotation functionality to the software, allowing users to rotate loaded images with specified rotation angles and pivot points. This enhances image orientation and position manipulation, expanding processing capabilities. Implementation: Use imrotate() in MATLAB or affine transformation matrices in VC++ with bilinear interpolation for smooth rotation. This feature will be taught and practiced during credit hours.