Image Denoising Using Wavelet Transform

In this text, we demonstrate how wavelet transform can be employed for image denoising to enhance image quality. The implementation typically begins with loading the image using functions like imread() and converting it to grayscale if necessary.

Firstly, we perform a 2-level wavelet decomposition using functions such as wavedec2() from wavelet toolboxes, which separates the image into approximation coefficients (low-frequency components) and detail coefficients (high-frequency components) at different scales. Hard thresholding (setting coefficients below threshold to zero) and soft thresholding (shrinking coefficients towards zero) are then applied using functions like wthresh() to remove high-frequency noise while preserving important image features. The threshold value can be determined using methods like VisuShrink or BayesShrink.

Additionally, we can explore alternative denoising techniques such as median filtering (using medfilt2()) for salt-and-pepper noise removal, or Gaussian filtering (using imgaussfilt()) for smoothing, which may further improve image quality and detail clarity depending on the noise characteristics.

Through these procedures and methods, we can effectively eliminate noise from images, resulting in clearer, more observable images with preserved edges and important features. The denoised image can be reconstructed using waverec2() and visualized with imshow() for quality assessment.