Thermal wave image deblurring based on depth residual network

Abstract Thermal wave imaging is a nondestructive testing (NDT) technology widely used to detect defects for various materials. It is important for quality control purposes to be able to clearly define the sizes of the defective areas. Due to the diffusive nature of thermal waves the acquired images contain varying degrees of blur depending on the depth of the defects, which severely affects the ability to define the defects. Conventional edge enhancement algorithms are hardly to achieve desirable results. Using deep convolutional neural network, we designed a deep residual network based on an encoder-decoder structure. Through the depth residual and skip-connection structures, we can effectively solve the vanishing gradient problem and improve the ability of feature extraction. The experimental results demonstrate that the proposed method shows superior performance over conventional image enhancement algorithms by providing richer information with higher contrast and more details.