TL;DR
SFSNet, which performs real-time frequency-spatial feature recovery for object detection under hazy and low-light conditions, and a Symmetric Frequency-Spatial Architecture is proposed to replace standard pooling with invertible Discrete Wavelet Transform for information-preserving decomposition.
This paper proposes FreqAdapt, a lightweight module for adaptive RAW data enhancement in the frequency domain that innovatively maps ISP operations to the Fourier frequency domain and performs domain separation based on the physical properties of ISP operations, ensuring each operation is performed in its most suitable...
For object detection in remote sensing images, foggy conditions tend to degrade image quality by scattering light and obscuring critical details, thereby compromising the performance of the involved detection models. To address this challenge, we first analyze the feature response of the Laplacian operation based on th...
Low-light object detection remains challenging due to severe illumination degradation, non-uniform local lighting, and amplified noise. Conventional low-light enhancement methods mainly operate in the spatial domain and often improve visual brightness without consistently benefiting downstream detection. In this paper,...
A Consistent Photometric Enhancement Network (CPEN) for change detection under complex illumination conditions that applies a low-light compensation mechanism to reduce photometric discrepancies between image pairs, which is followed by a bi-temporal enhancement module that jointly improves brightness while preserving...
Remote sensing object detection is a fundamental task in ground scene observation and analysis. Despite the currently discrete-frame detectors achieves remarkable performance, they still suffer from three critical limitations: 1) mainstream architectures regress spatial locations independently, making it difficult to e...
DPSF-Net is proposed, a dual-prior spatial-frequency network built on MCAF-Net for real-world RSID that achieves state-of-the-art performance on the real-world RRSHID remote sensing image dehazing benchmark and remains competitive across multiple synthetic datasets.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
