Image Stitching Techniques Using Wavelet Transform

This article explores the implementation of image stitching techniques using wavelet transform in MATLAB. Wavelet transform represents an advanced signal and image processing technology that decomposes signals or images into multiple wavelets at different frequencies for detailed analysis. The multiresolution characteristics of wavelet transform make it particularly suitable for image stitching applications. In MATLAB implementation, the wavelet decomposition process typically involves using functions like wavedec2() for 2D discrete wavelet transform, which separates images into approximation and detail coefficients at multiple resolution levels. For horizontal stitching, the algorithm aligns corresponding wavelet coefficients from different images and reconstructs them using waverec2(). This technique effectively combines multiple images into a larger composite image while preserving edge details and minimizing seam artifacts. Key implementation aspects include selecting appropriate wavelet families (such as Haar, Daubechies, or Symlets), determining optimal decomposition levels, and developing coefficient fusion strategies. The MATLAB implementation provides a robust solution for creating panoramic images or large-scale image composites, making it an excellent choice for applications requiring seamless image integration.