Title :
Practical application of random forests for super-resolution imaging
Author :
Jun-Jie Huang ; Wan-Chi Siu
Author_Institution :
Dept. of Electron. & Inf. Eng., Hong Kong Polytech. Univ., Hong Kong, China
Abstract :
In this paper, a novel learning-based single image super-resolution method using random forest is proposed. Different from example-based super-resolution methods which search for similar image patches from an external database or the input image, and the sparse representation model based methods which rely on the sparse representation, this proposed super-resolution with random forest (SRRF) method takes the divide-and-conquer strategy. Random forest is applied to classify the training LR-HR patch pairs into a number of classes. Within every class, a simple linear regression model is used to model the relationship between the LR image patches and their corresponding HR image patches. Experimental results show that the proposed SRRF method can generate the state-of-the-art super-resolved images with near real-time performance.
Keywords :
divide and conquer methods; image classification; image resolution; learning (artificial intelligence); regression analysis; SRRF method; divide-and-conquer strategy; learning-based single image super-resolution method; linear regression model; super-resolution imaging; super-resolution-with-random forest method; training LR-HR patch pair classification; Decision trees; Image edge detection; Image resolution; Linear regression; Signal resolution; Training; Training data; Image processing; fast approach; learning; random forest; super-resolution;
Conference_Titel :
Circuits and Systems (ISCAS), 2015 IEEE International Symposium on
Conference_Location :
Lisbon
DOI :
10.1109/ISCAS.2015.7169108