Region-Based Removal of Thermal Reflection Using Pruned Fully Convolutional Network

Abstract

In general, an image obtained from a thermal camera often has a mirror reflection or shadow reflected off the ground around an object, which is referred to as thermal reflection. Sometimes the thermal reflections are connected to their objects in images, which makes it difficult to detect or recognize the object only. Thermal reflections sometimes occur on the wall near an object and are detected as another object when they are not connected to the object. Furthermore, the size of thermal reflection and pixel value significantly vary with the medium of the reflected range and the surrounding temperature. In these cases, the patterns and pixel values of thermal reflection and the object become similar and difficult to distinguish. However, there are insufficient studies on removing the thermal reflection of various kinds of objects in diverse environments. Therefore, in this paper, we propose a pruned fully convolutional network (PFCN)-based method for removing the thermal reflection of an object using the surrounding information when image transformation is performed only within the region of an object. When experiments were conducted using self-collected databases (Dongguk thermal image database (DTh-DB) and Dongguk items & vehicles database (DI&V-DB)) and open databases, the method proposed herein exhibited more outstanding performance in removing thermal reflection when compared with the state-of-the-art methods.