Multi-focus images using ResNet-based fusion techniques

The Multi-Focus Image Fusion (MFIF) technique integrates various source images into a single clearer image, with the identification of clear points being crucial for effective fusion. Traditional edge-preserving filters struggle with scale-dependent edge identification and necessitate guide images. This research introduces a ResNet-based image super-resolution technique paired with Non-Subsampled Shearlet Transform (NSST) Coefficient Extraction and Energy of Gradient (EOG) focus measurement. It employs a small area elimination process and a rolling guidance filter to enhance the initial decision map. The fusion of weighted source images and decision maps yields superior results, with performance metrics like MI of 9.3670, QY of 0.9770, and a runtime of 60.23 s, outperforming existing models such as SR, BF, and CNN variants. This framework significantly enhances resolution while preserving structural details in multi-focus image fusion.