CNN and Gaussian Pyramid-Based Approach For Enhance Multi-Focus Image Fusion

Abstract

Achieving sharp focus in both foreground and background areas simultaneously in digital photography is challenging due to the limited depth of field (DOF) in DSLR cameras. This often results in blurred images with isolated details, hindering the capture of clear, high-quality photographs. Existing multi-focus image fusion models struggle with issues related to image quality and managing input images taken from varying angles. To address these challenges, we introduce a novel multi-focus image fusion method that integrates Convolutional Neural Networks (CNNs) with Gaussian Pyramid techniques. Our approach follows a four-step process: visual search, initial segmentation, compression analysis, and final synthesis. The Gaussian pyramid technique enhances edge detection by progressively reducing the image size and facilitating the identification of automatic objects. By leveraging these combined techniques, our method ensures the generation of uniformly focused images. We rigorously evaluate our model using metrics such as Perceptual Image Quality Evaluator (PIQE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and connectivity metrics. Our enhanced model demonstrates superior performance, achieving a 4.70% improvement in PIQE compared to previous CNN-based methods. This confirms the effectiveness of our approach in producing clear, high-quality fused images.