TY - JOUR
T1 - Region-Based Removal of Thermal Reflection Using Pruned Fully Convolutional Network
AU - Batchuluun, Ganbayar
AU - Baek, Na Rae
AU - Nguyen, Dat Tien
AU - Pham, Tuyen Danh
AU - Park, Kang Ryoung
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - image transform
KW - pruned fully convolutional network
KW - Thermal image
KW - thermal reflection removal
UR - http://www.scopus.com/inward/record.url?scp=85084439479&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2020.2988931
DO - 10.1109/ACCESS.2020.2988931
M3 - Article
AN - SCOPUS:85084439479
SN - 2169-3536
VL - 8
SP - 75741
EP - 75760
JO - IEEE Access
JF - IEEE Access
M1 - 9072376
ER -