Implementation of Infrared Image Enhancement

In this paper, we can further explore the implementation process of multiresolution decomposition and how this technique enhances infrared images. We can discuss various multiresolution decomposition methods such as wavelet transforms(using functions like wavedec2 in MATLAB for 2D decomposition) and Laplacian pyramid decomposition, along with their respective advantages and disadvantages in terms of computational efficiency and preservation of image details. Additionally, we can examine how these methods improve image contrast through implementation of algorithms like adaptive histogram equalization(using adapthisteq function with clip limit parameter control) and enhanced edge detection algorithms(Sobel, Canny operators with threshold optimization). The implementation typically involves decomposing the image into different frequency subbands, processing each subband separately, and then reconstructing the enhanced image using inverse transforms. Finally, we can briefly introduce applications of multiresolution decomposition technology in other fields such as medical imaging and remote sensing, demonstrating its importance and versatility across different domains.