תפריט נגישות
ניגודיות עדינהניגודיות גבוהה
מונוכרום
הדגשת קישורים
חסימת אנימציה
פונט קריא
סגור
איפוס הגדרות נגישות
להורדת מודול נגישות חינם תפריט נגישותניגודיות עדינהניגודיות גבוההמונוכרוםהדגשת קישוריםחסימת אנימציהפונט קריאסגוראיפוס הגדרות נגישותלהורדת מודול נגישות חינםNAVOT OZ - Department of Sensing, Information and Mechanization Engineering, Institute of Agricultural Engineering, Agricultural Research Organization (ARO),Volcani Center, Rishon LeZion, Israel; School of Electrical Engineering, Tel Aviv University, Tel Aviv 69978, Israel.
NIR SOCHEN - Department of Applied Mathematics, Tel Aviv University, Tel Aviv 69978, Israel
OSHRY MARKOVICH - Rahan Meristem, Kibbutz Rosh Hanikra Western Galilee 22825, Israel
ZIV HALAMISH - Evogene, Gad Feinstein St., Rehovot 7612002, Israel
LENA SHPIALTER-KAROL - Hazera, Berurim M.P Shikmim 7983700, Israel
Infrared (IR) imagery is used in agriculture for irrigation monitoring and early detection of disease in plants. The common IR cameras in this field typically have low resolution. This work offers a method to obtain the super-resolution of IR images from low-power devices to enhance plant traits. The method is based on deep learning (DL). Most calculations are done in the low-resolution domain. The results of each layer are aggregated together to allow a better flow of information through the network. This work shows that good results can be achieved using depthwise separable convolution with roughly 300K multiply-accumulate computations (MACs), while state-of-the-art convolutional neural network-based super-resolution algorithms are performed with around 1500K MACs. MTF analysis of the proposed method shows a real ×4 improvement in the spatial resolution of the system, out-preforming the diffraction limit. The method is demonstrated on real agricultural images.
NAVOT OZ - Department of Sensing, Information and Mechanization Engineering, Institute of Agricultural Engineering, Agricultural Research Organization (ARO),Volcani Center, Rishon LeZion, Israel; School of Electrical Engineering, Tel Aviv University, Tel Aviv 69978, Israel.
NIR SOCHEN - Department of Applied Mathematics, Tel Aviv University, Tel Aviv 69978, Israel
OSHRY MARKOVICH - Rahan Meristem, Kibbutz Rosh Hanikra Western Galilee 22825, Israel
ZIV HALAMISH - Evogene, Gad Feinstein St., Rehovot 7612002, Israel
LENA SHPIALTER-KAROL - Hazera, Berurim M.P Shikmim 7983700, Israel
Infrared (IR) imagery is used in agriculture for irrigation monitoring and early detection of disease in plants. The common IR cameras in this field typically have low resolution. This work offers a method to obtain the super-resolution of IR images from low-power devices to enhance plant traits. The method is based on deep learning (DL). Most calculations are done in the low-resolution domain. The results of each layer are aggregated together to allow a better flow of information through the network. This work shows that good results can be achieved using depthwise separable convolution with roughly 300K multiply-accumulate computations (MACs), while state-of-the-art convolutional neural network-based super-resolution algorithms are performed with around 1500K MACs. MTF analysis of the proposed method shows a real ×4 improvement in the spatial resolution of the system, out-preforming the diffraction limit. The method is demonstrated on real agricultural images.
