Image Denoising Program Using Wavelet and DCT Transforms

This article explores image denoising techniques utilizing wavelet transform and Discrete Cosine Transform (DCT). These methods effectively reduce noise in digital images while preserving important image features. The wavelet transform approach typically involves thresholding wavelet coefficients at different decomposition levels, where hard or soft thresholding functions are applied to suppress noise components. The DCT-based method operates by transforming image blocks into frequency domain and applying frequency-selective filtering to attenuate noise patterns. We provide concrete implementation examples demonstrating how to apply these transforms using MATLAB's wavelet toolbox (e.g., wavedec2/wavecrec2 functions for 2D wavelet decomposition/reconstruction) and DCT processing (using dct2/idct2 functions). The implementation includes parameter optimization for threshold selection and block size determination for DCT processing. Our validation tests confirm the effectiveness of both techniques through quantitative metrics (PSNR, SSIM) and visual quality assessment. The code structure includes main denoising functions, parameter configuration modules, and result evaluation components. You can confidently apply these proven methods to your image processing tasks to achieve significantly improved results.