A Multiscale Method for Infrared Ship Detection Based on Morphological Reconstruction and Two-Branch Compensation Strategy.

Infrared ship target detection is crucial technology in marine scenarios. Ship targets vary in scale throughout navigation because the distance between the ship and the infrared camera is constantly changing. Furthermore, complex backgrounds, such as sea clutter, can cause significant interference during detection tasks.

Transformer-based multilevel region and edge aggregation network for magnetic resonance image segmentation.

To improve the quality of magnetic resonance (MR) image edge segmentation, some researchers applied additional edge labels to train the network to extract edge information and aggregate it with region information. They have made significant progress. However, due to the intrinsic locality of convolution operations, the convolution neural network-based region and edge aggregation has limitations in modeling long-range information.

Multiresolution Mutual Assistance Network for Cardiac Magnetic Resonance Images Segmentation.

The automatic segmentation of cardiac magnetic resonance (MR) images is the basis for the diagnosis of cardiac-related diseases. However, the segmentation of cardiac MR images is a challenging task due to the inhomogeneity of MR images intensity distribution and the unclear boundaries between adjacent tissues. In this paper, we propose a novel multiresolution mutual assistance network (MMA-Net) for cardiac MR images segmentation.

A Fast Adaptive Multi-Scale Kernel Correlation Filter Tracker for Rigid Object.

The efficient and accurate tracking of a target in complex scenes has always been one of the challenges to tackle. At present, the most effective tracking algorithms are basically neural network models based on deep learning. Although such algorithms have high tracking accuracy, the huge number of parameters and computations in the network models makes it difficult for such algorithms to meet the real-time requirements under limited hardware conditions, such as embedded platforms with small size, low power consumption and limited computing power.

Low-Light Image Enhancement Using Photometric Alignment with Hierarchy Pyramid Network.

Low-light image enhancement can effectively assist high-level vision tasks that often fail in poor illumination conditions. Most previous data-driven methods, however, implemented enhancement directly from severely degraded low-light images that may provide undesirable enhancement results, including blurred detail, intensive noise, and distorted color. In this paper, inspired by a coarse-to-fine strategy, we propose an end-to-end image-level alignment with pixel-wise perceptual information enhancement pipeline for low-light image enhancement.

Multiresolution Aggregation Transformer UNet Based on Multiscale Input and Coordinate Attention for Medical Image Segmentation.

The latest medical image segmentation methods uses UNet and transformer structures with great success. Multiscale feature fusion is one of the important factors affecting the accuracy of medical image segmentation. Existing transformer-based UNet methods do not comprehensively explore multiscale feature fusion, and there is still much room for improvement.