Curvelet Transform-Based Image Processing with Threshold Reconstruction

This article demonstrates the application of curvelet transform for image processing, involving transformation of an input image and visualization of its coefficient distribution. The implementation typically utilizes the CurveLab toolbox in MATLAB, where key functions like fdct_wrapping() perform the forward curvelet transform. Following coefficient visualization, an appropriate threshold is selected (using methods like hard or soft thresholding) to eliminate noise while preserving significant coefficients. The reconstruction phase employs inverse curvelet transform functions (ifdct_wrapping()) to regenerate the image. This approach enhances the clarity of image details and features, facilitating better understanding of image content. The reconstructed image can undergo further analysis and processing (such as feature extraction or segmentation) to derive additional valuable information. Thus, this methodology aims to improve accuracy and efficiency in image analysis through curvelet transform techniques, providing valuable references for subsequent research. The process involves parameter tuning for optimal threshold selection and evaluation metrics (e.g., PSNR) to quantify reconstruction quality.