Dynamic Thermal Image Enhancement Using Wavelet-Based Adaptive Fusion

The research introduces a wavelet-based adaptive fusion approach for thermal image enhancement that delivers superior outcomes compared to conventional strategies, including HE, CLAHE and gamma correction when considering edge preservation and details. The method utilizes wavelet transformation to enhance contrast with fine-scale preservation and noise elimination, making it appropriate for real-time applications. Experimental evaluation shows that adaptive Fusion provides a PSNR of 32.78 dB and MSE of 159.45, together with an SSIM of 0.792. An entropy of 8.15 also represents good image information preservation. The presented method demonstrates robust capabilities for monitoring tasks and medical diagnosis alongside industrial inspecting operations and reduces computational difficulties commonly found in previous methodologies.