This paper surveys the extensive usage of pulse coupled neural networks. The visual cortex system of mammalians was the backbone for the development of pulse coupled neural network. PCNN (Pulse Coupled Neural Networks) is unique from other techniques due to its synchronous pulsed output, adjustable threshold and controllable parameters. is Hence the uniqueness of this network utilized in the fields of image processing. The basic model of PCNN and the consecutive changes implemented, to strengthen the pulse coupled neural network are discussed initially. Then the applications of PCNN are broadly discussed. The other miscellaneous applications utilizing pulse coupled neural networks are thrown light in the last section.

Review: Pulse coupled neural networks and its applications

Major technical constraints like minimum data storage at satellite platform in space, less bandwidth for communication with earth station, etc. limits the satellite sensors from capturing images with high spatial and high spectral resolutions simultaneously. To overcome this limitation, image fusion has proved to be a potential tool in remote sensing applications which integrates the information from combinations of panchromatic, multispectral or hyperspectral images; intended to result in a composite image having both higher spatial and higher spectral resolutions. The research in this area cites date back to last few decades, but the diverse approaches proposed so far by different researchers have been rarely discussed at one place. This paper is an honest attempt to collectively discuss all possible algorithms along with quality metrics following two assessment procedures i.e. at full and reduced scale resolutions to evaluate performance of these algorithms.

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Image Fusion in Remote Sensing Applications: A Review

Recently, many researchers have paid their more attention to image fusion technique based on pulse coupled neural network (PCNN). In order to make the researchers to rapidly understand the research development of image fusion basedonPCNN,itissystematicallyreviewedinthepaper.On the basis of statistical analysis on published papers, firstly, PCNN and some modified models are introduced. Then we review the PCNN's applications in the field of image fusion. Subsequently,someexistingproblemsaresummarized,while we give some suggestions for the future research.

Review of Image Fusion Based on Pulse-Coupled Neural Network

Ripplet domain fusion approach for CT and MR medical image information

Multi-focus: Focused region finding and multi-scale transform for image fusion

Image Fusion Algorithm Based on Simplified PCNN in Nonsubsampled Contourlet Transform Domain

Pedestrian re-identification is an integral part of surveillance video analysis, and the field of pedestrian re-identification has been growing rapidly in recent years with the development of deep learning. Most existing pedestrian re-identification methods are based on convolutional neural network deep learning models, and most studies consider learning representations from individual images, ignoring the potential interactions between them. In this paper, we propose a dual feature-enhanced pedestrian re-identification method based on Transformer architecture, which learns high discriminative features of pedestrians, suppresses the influence of irrelevant features on the network, and explicitly models the interactions between all input images to more fully learn the pedestrian feature representations from different perspectives, thus making the network more accurate and robust in overall recognition. Experiments with the model on four publicly available pedestrian re-identification datasets demonstrate that all evaluation metrics are higher than most of the current mainstream pedestrian re-identification models.

Jin Li

Papers

Image Fusion Algorithm Based on Simplified PCNN in Nonsubsampled Contourlet Transform Domain

Transformer dual feature enhancement based pedestrian re-identification algorithm