Two Image Denoising Methods Based on Non-Subsampled Transform

The text introduces two image denoising techniques based on non-subsampled transform, both exhibiting better performance than traditional contourlet transform methods. Let's examine the principles and advantages of these approaches in detail.

The first method employs wavelet transform for image denoising. Wavelet transform is a mathematical tool that decomposes signals or images into different frequency subbands. Through threshold processing of these subbands, image noise can be effectively removed, thereby enhancing image quality. In code implementation, this typically involves applying discrete wavelet transform (DWT) using functions like wavedec2() in MATLAB, followed by thresholding operations using soft or hard threshold functions. Compared to other transform methods, wavelet transform better preserves image details while effectively removing noise.

The second method utilizes adaptive filtering for image denoising. This approach analyzes local image characteristics to automatically adjust filter parameters, providing better adaptation to image content and noise properties. In practical implementation, this often involves algorithms like Wiener filtering or locally adaptive thresholding, where functions such as wiener2() in MATLAB can be employed. Unlike traditional fixed-filter methods, adaptive filtering achieves more precise noise suppression while preserving finer image details through dynamic parameter adjustment based on local statistics.

In summary, these two non-subsampled transform-based image denoising methods demonstrate excellent performance in noise removal, proving more effective than contourlet transform approaches. By selecting appropriate methods based on specific image characteristics, we can obtain clearer, higher-quality images through proper algorithm implementation.