Image Compression, Denoising, Enhancement and Sharpening

MATLAB 11K 305 views 0 downloads 1 credits
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Resource Overview

This comprehensive image processing program demonstrates techniques for compression, denoising, enhancement and sharpening. Implementation includes: displaying digital image matrix data and its Fourier transform, image compression using 2D discrete cosine transform (DCT), contrast enhancement via grayscale transformation, salt-and-pepper noise removal using medfilt2 2D median filtering, mean filtering with filter2 for noise reduction, adaptive Wiener filtering, five distinct gradient enhancement methods for sharpening, high-pass filtering with mask processing, Butterworth low-pass filter for image smoothing, and Butterworth high-pass filter for sharpening operations.

Detailed Documentation

This image compression, denoising, enhancement and sharpening program is provided for reference. The implementation includes the following components:

- Display of digital image matrix data and its Fourier transform spectrum analysis

- Image compression using 2D discrete cosine transform (DCT) with quantization techniques

- Contrast enhancement through grayscale transformation methods including histogram equalization

- Salt-and-pepper noise filtering using MATLAB's medfilt2 function for 2D median filtering

- Mean filtering implementation using MATLAB's filter2 function to reduce random noise

- Adaptive Wiener filtering for statistical noise removal based on local image statistics

- Image sharpening using five different gradient enhancement methods (Sobel, Prewitt, Roberts, etc.)

- High-pass filtering combined with mask processing for edge enhancement

- Image smoothing using Butterworth low-pass filter with adjustable cutoff frequency

- Image sharpening using Butterworth high-pass filter for frequency-domain enhancement

We hope this implementation provides valuable insights into image processing techniques!

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