Wavelet-Based Image Denoising Processing

In this document, we can perform the following processing methods to improve image quality:

1. Wavelet-based image denoising processing: By eliminating noise points and interference in images, this method makes images clearer and easier to analyze. Implementation typically involves wavelet decomposition using functions like wavedec2(), thresholding techniques (hard/soft thresholding) using wthresh(), and reconstruction using waverec2() to preserve important image features while removing noise.

2. Wavelet-based image compression processing: By reducing the storage space occupied by images, this approach makes image transmission and storage more efficient. The process involves wavelet transformation using dwt2(), quantization of wavelet coefficients, and entropy coding techniques that leverage the energy compaction property of wavelets in image compression algorithms.

3. Wavelet-based image enhancement processing: By enhancing image characteristics such as contrast and brightness, this technique makes images more vivid and visually impactful. Implementation often includes multi-scale analysis using wavelet transforms, histogram equalization applied to specific frequency bands, and coefficient modification through functions like wcodemat() to enhance desired features.

These processing methods can effectively improve image quality and meet the requirements of different application scenarios. By comprehensively applying these techniques, we can achieve superior image results through appropriate parameter tuning and algorithm selection.