Traditional feature matching methods, such as scale-invariant feature transform (SIFT),
usually use image intensity or gradient information to detect and describe feature points; …

Image quality assessment (IQA) aims to use computational models to measure the image
quality consistently with subjective evaluations. The well-known structural similarity index …

Multi-modal remote sensing image (MRSI) has nonlinear radiation distortion (NRD) and
significant contrast differences to which image gradient features are usually sensitive …

Traditional image steganography often leans interests towards safely embedding hidden
information into cover images with payload capacity almost neglected. This paper combines …

Recognizing places using Lidar in large-scale environments is challenging due to the
sparse nature of point cloud data. In this letter we present BVMatch, a Lidar-based frame-to …

This paper presents a method for retinal vasculature extraction based on biologically
inspired multi-orientation analysis. We apply multi-orientation analysis via so-called …

Robust and reliable multi-modal image matching is an essential and challenging technique
in the multi-modality involved scenarios for image fusion, image mosaic, and visual …

Existing deep learning-based full-reference IQA (FR-IQA) models usually predict the image
quality in a deterministic way by explicitly comparing the features, gauging how severely …

Local-feature-based face recognition (FR) methods, such as Gabor features encoded by
local binary pattern, could achieve state-of-the-art FR results in large-scale face databases …

Today, multiresolution (MR) transforms are a widespread tool for image fusion. They
decorrelate the image into several scaled and oriented sub-bands, which are usually …