A Fast Statistical Method for Multilevel Thresholding in Wavelet Domain

TL;DR

This paper introduces a fast wavelet domain multilevel thresholding algorithm that improves segmentation efficiency and quality, capturing significant image details with fewer coefficients and outperforming existing space domain methods.

Contribution

It presents a novel wavelet domain segmentation method with variable block sizes that reduces time complexity and enhances image quality over prior algorithms.

Findings

Less time complexity compared to space domain algorithms

Achieves higher PSNR and SSIM indices

Uses only 16 wavelet coefficients at threshold level 3

Abstract

An algorithm is proposed for the segmentation of image into multiple levels using mean and standard deviation in the wavelet domain. The procedure provides for variable size segmentation with bigger block size around the mean, and having smaller blocks at the ends of histogram plot of each horizontal, vertical and diagonal components, while for the approximation component it provides for finer block size around the mean, and larger blocks at the ends of histogram plot coefficients. It is found that the proposed algorithm has significantly less time complexity, achieves superior PSNR and Structural Similarity Measurement Index as compared to similar space domain algorithms[1]. In the process it highlights finer image structures not perceptible in the original image. It is worth emphasizing that after the segmentation only 16 (at threshold level 3) wavelet coefficients captures the significant variation of image.