Fast weighted least squares for detail and tone enhancement of medical images

Abstract

Background The limitations in current medical imaging systems often result in poor visibility and insufficient detail. Such low-quality images hinder the decision-making of medical professionals. The weighted least squares (WLS) model provides a prevalent framework for detail and tone manipulation, but it is non-trivial to solve. Method We propose a fast solution to the WLS model. Instead of addressing a large linear system or approximating two-dimensional image processing with multiple one-dimensional procedures, we demonstrate that the model can be solved efficiently using an iterative algorithm. We leverage our method for the detail and tone enhancement of medical images. Results (1) For detail enhancement, on the three subsets of X-ray images, the spatial-spectral entropy-based quality (SSEQ) values are 21.15, 29.46, and 31.36; the convolutional neural networks image quality assessment (CIQA) values are 17.67, 25.60, and 26.01; on the computed tomography and magnetic resonance imaging images, the SSEQ and CIQA values are 14.01 and 16.93; it achieves peak signal-to-noise ratio (PSNR) ≥39.39 and structural similarity index (SSIM) ≥0.99; (2) for tone enhancement, the tone mapped image quality index, fidelity, and naturalness values are recorded at 0.7905, 0.9300, and 0.0090; it achieves PSNR ≥50.09 and SSIM ≥0.99. Notably, our method processes a 720P color image in 0.0725 seconds on a modern graphics processing unit. Conclusion The proposed method enhances the detail and tone of medical images. It addresses computational challenges inherent in traditional approaches. Its adaptability to diverse applications suggests a promising avenue for improving diagnostic accuracy and patient outcomes in clinical practice.