Image Superresolution Reconstruction via Granular Computing Clustering

Abstract

The problem of generating a superresolution (SR) image from a single low-resolution (LR) input image is addressed via granular computing clustering in the paper. Firstly, and the training images are regarded as SR image and partitioned into some SR patches, which are resized into LS patches, the training set is composed of the SR patches and the corresponding LR patches. Secondly, the granular computing (GrC) clustering is proposed by the hypersphere representation of granule and the fuzzy inclusion measure compounded by the operation between two granules. Thirdly, the granule set (GS) including hypersphere granules with different granularities is induced by GrC and used to form the relation between the LR image and the SR image by lasso. Experimental results showed that GrC achieved the least root mean square errors between the reconstructed SR image and the original image compared with bicubic interpolation, sparse representation, and NNLasso.

Analysis and tracking of animal movements through granulation of temporal domain (GTD)

“…By RGB2GRAY, a pre-defined proce-dure that converts colorful video to gray. Then, frames digitize the gray level video output, i.e., the software analyzes individual frames from various background-subtraction algo-rithms so that objects are easily tracked from the background [3], [19]. The proposed method creates motion image from con-secutive pair of frames.…”

“…While detecting objects [2], [19], the proposed model tracks a single-target object continuously, and accurately measures the distance moved by the target. The objective of single-target tracking is to associate targeted object to the consecutive video frames identified by the video-frame sequences [17].…”