Gradient based restoration of coal mine images obtained by underground wireless transmissions

Abstract

Curvature-driven diffusion (CDD) principles were used to develop a novel gradient based image restoration algorithm. The algorithm fills in blocks of missing data in a wireless image after transmission through the network. When images are transmitted over fading channels, especially in the severe circumstances of a coal mine, blocks of the image may be destroyed by the effects of noise. Instead of using common retransmission query protocols the lost data is reconstructed by using the adaptive curvature-driven diffusion (ACDD) image restoration algorithm in the gradient domain of the destroyed image. Missing blocks are restored by the method in two steps: In step one, the missing blocks are filled in the gradient domain by the ACDD algorithm; in step two, and the image is reconstructed from the reformed gradients by solving a Poisson equation. The proposed method eliminates the staircase effect and accelerates the convergence rate. This is demonstrated by experimental results.