Recently infrared small target detection (IRSTD) has been dominated by deep-learning-
based methods. However these methods mainly focus on the design of complex model …

Dilated convolution which expands the receptive field by inserting gaps between its
consecutive elements is widely employed in computer vision. In this study we propose three …

Foggy-scene semantic segmentation (FSSS) is highly challenging due to the diverse effects
of fog on scene properties and the limited training data. Existing research has mainly …

Abstract Segment Anything Model (SAM) has emerged as a transformative approach in
image segmentation acclaimed for its robust zero-shot segmentation capabilities and …

Monocular depth estimation has experienced significant progress on terrestrial images in
recent years thanks to deep learning advancements. But it remains inadequate for …

Calibration-based methods have dominated RAW image denoising under extremely low-
light environments. However, these methods suffer from several main deficiencies: 1) the …

High dynamic range (HDR) images can record much more intensity levels than usual ones.
Existing methods mainly reconstruct HDR images from the 8-bit low dynamic range (LDR) …

Dense image prediction tasks demand features with strong category information and precise
spatial boundary details at high resolution. To achieve this, modern hierarchical models …

Recently deep neural networks have achieved excellent performance on low-light raw video
enhancement. However they often come with high computational complexity and large …

The design of deep network architectures and training methods in computer vision has been
well-explored. However in almost all cases the images have been used as provided with …