De-Fencing and Multi-Focus Fusion Using Markov Random Field and Image Inpainting

Abstract

Multi-focus image fusion aims at combining source information from differently focused images. Fusion of multi-focus images has great applications in machine vision. The paper focuses on removal of fence occlusions in multi-focus images. The proposed model extracts fence occlusion map using salient image features, and refined by morphological operators. Binary operators, and inpainting methods are used for fence removal and restoration. The proposed model estimates the fence area using statistical characteristics in focused regions. Similarly, binary filtering is used to perform thinning of enlarged areas for optimised restoration. The proposed model employs guided filtering for consistency verification. Fusion and restoration results are compared using several (reference and no-reference based) image quality metrics. Simulations show that proposed scheme achieves better results (visually and quantitatively) as compared to existing state-of-the-art techniques.