9 accepted papers
Low-light image enhancement methods based on diffusion models, though effective in improving image quality, often overrely on noise sensitivity and neglect the reconstruction deviations due to the naive up- and down-sampling operations. To address this issue, we propose a novel diffusion model, MWT-…
False correspondence removal is a persistent challenge in image feature-matching-based applications, especially in complex scenes. Traditional methods often rely on the consistency assumption to model the motion of correct correspondences, which neglects non-consistent correct correspondences, resul…
Accurately computing optical flow in low-contrast and noisy dark images is challenging, especially when contour information is degraded or difficult to extract. This paper proposes CEDFlow, a latent space contour enhancement for estimating optical flow in dark environments. By leveraging spatial fre…
Large-scale place recognition based on point clouds remains a challenge due to the geometric transformations in the scene. Among many geometric transformations, rotation is particularly challenging to handle in practice. Only a few works try to encode rotation invariance into learning based place re
Infrared and visible image fusion is an important multimodal image processing task that aims to enhance computer vision performance by effectively fusing infrared and visible images. Although in recent years, many deep learning-based methods for infrared and visible image fusion have emerged. Howeve…
Photographs taken in low-illumination environment have a low signal-to-noise ratio and impaired visual quality. Enhancing lowlight images tends to amplify noise. To address this problem, we propose a Multi-Scale Recursive Feature Enhancement (MSRFE) network for low light image enhancement. The MSRFE…
Low light images suffer from low dynamic range and severe noise due to low signal-to-noise ratio (SNR). In this paper, we propose joint enhancement and denoising of low light images via just-noticeable-difference (JND) transform. We achieve contrast enhancement and noise reduction simultaneously bas…
In low light condition, the signal-to-noise ratio (SNR) is low and thus the captured images are seriously degraded by noise. Since low light images contain much noise in flat and dark regions, contrast enhancement without considering noise characteristics causes serious noise amplification. In this…
Image quality of mobile displays are significantly influenced by ambient light. In the daylight condition, displayed images on mobile displays are darkly perceived by human visual system (HVS), which suffer from significant detail loss. However, only luminance enhancement seriously affects image det…