Recently, a number of double-image cryptosystems have been developed. However, there are notable security performance differences between the two encryption channels in these algorithms. This weakness downgrades the security level and practicability of these cryptosystems, as the cryptosystems cannot guarantee all the input images be transmitted in the channel with higher security level. In this paper, we propose a novel double-image encryption scheme based on cross-image pixel scrambling in gyrator domains. The two input images are firstly shuffled by the proposed cross-image pixel scrambling approach, which can well balance the pixel distribution across the input images. The two scrambled images will be encoded into the real and imaginary parts of a complex function, and then converted into gyrator domains. An iterative architecture is designed to enhance the security level of the cryptosystem, and the cross-image pixel scrambling operation is performed to the real and imaginary parts of the generated complex encrypted data in each round. Numerical simulation results prove that a satisfactory and balanced security performance can be achieved in both channels.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1364/OE.22.007349 | DOI Listing |
IEEE Trans Pattern Anal Mach Intell
August 2024
This work studies the problem of image semantic segmentation. Current approaches focus mainly on mining "local" context, i.e.
View Article and Find Full Text PDFIEEE Trans Pattern Anal Mach Intell
August 2023
The rapid development of deep learning has made a great progress in image segmentation, one of the fundamental tasks of computer vision. However, the current segmentation algorithms mostly rely on the availability of pixel-level annotations, which are often expensive, tedious, and laborious. To alleviate this burden, the past years have witnessed an increasing attention in building label-efficient, deep-learning-based image segmentation algorithms.
View Article and Find Full Text PDFIEEE Trans Med Imaging
June 2023
We present a novel deep network (namely BUSSeg) equipped with both within- and cross-image long-range dependency modeling for automated lesions segmentation from breast ultrasound images, which is a quite daunting task due to (1) the large variation of breast lesions, (2) the ambiguous lesion boundaries, and (3) the existence of speckle noise and artifacts in ultrasound images. Our work is motivated by the fact that most existing methods only focus on modeling the within-image dependencies while neglecting the cross-image dependencies, which are essential for this task under limited training data and noise. We first propose a novel cross-image dependency module (CDM) with a cross-image contextual modeling scheme and a cross-image dependency loss (CDL) to capture more consistent feature expression and alleviate noise interference.
View Article and Find Full Text PDFIEEE Trans Pattern Anal Mach Intell
July 2023
Semi-supervised semantic segmentation aims to learn a semantic segmentation model via limited labeled images and adequate unlabeled images. The key to this task is generating reliable pseudo labels for unlabeled images. Existing methods mainly focus on producing reliable pseudo labels based on the confidence scores of unlabeled images while largely ignoring the use of labeled images with accurate annotations.
View Article and Find Full Text PDFIEEE J Biomed Health Inform
January 2023
Accurate tissue segmentation in histopathological images is essential for promoting the development of precision pathology. However, the size of the digital pathological image is great, which needs to be tiled into small patches containing limited semantic information. To imitate the pathologist's diagnosis process and model the semantic relation of the whole slide image, We propose a semi-supervised pixel contrastive learning framework (SSPCL) which mainly includes an uncertainty-guided mutual dual consistency learning module (UMDC) and a cross image pixel-contrastive learning module (CIPC).
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!