2,338 research outputs found
Global Structure-Aware Diffusion Process for Low-Light Image Enhancement
This paper studies a diffusion-based framework to address the low-light image
enhancement problem. To harness the capabilities of diffusion models, we delve
into this intricate process and advocate for the regularization of its inherent
ODE-trajectory. To be specific, inspired by the recent research that low
curvature ODE-trajectory results in a stable and effective diffusion process,
we formulate a curvature regularization term anchored in the intrinsic
non-local structures of image data, i.e., global structure-aware
regularization, which gradually facilitates the preservation of complicated
details and the augmentation of contrast during the diffusion process. This
incorporation mitigates the adverse effects of noise and artifacts resulting
from the diffusion process, leading to a more precise and flexible enhancement.
To additionally promote learning in challenging regions, we introduce an
uncertainty-guided regularization technique, which wisely relaxes constraints
on the most extreme regions of the image. Experimental evaluations reveal that
the proposed diffusion-based framework, complemented by rank-informed
regularization, attains distinguished performance in low-light enhancement. The
outcomes indicate substantial advancements in image quality, noise suppression,
and contrast amplification in comparison with state-of-the-art methods. We
believe this innovative approach will stimulate further exploration and
advancement in low-light image processing, with potential implications for
other applications of diffusion models. The code is publicly available at
https://github.com/jinnh/GSAD.Comment: Accepted to NeurIPS 202
Learning Light Field Angular Super-Resolution via a Geometry-Aware Network
The acquisition of light field images with high angular resolution is costly.
Although many methods have been proposed to improve the angular resolution of a
sparsely-sampled light field, they always focus on the light field with a small
baseline, which is captured by a consumer light field camera. By making full
use of the intrinsic \textit{geometry} information of light fields, in this
paper we propose an end-to-end learning-based approach aiming at angularly
super-resolving a sparsely-sampled light field with a large baseline. Our model
consists of two learnable modules and a physically-based module. Specifically,
it includes a depth estimation module for explicitly modeling the scene
geometry, a physically-based warping for novel views synthesis, and a light
field blending module specifically designed for light field reconstruction.
Moreover, we introduce a novel loss function to promote the preservation of the
light field parallax structure. Experimental results over various light field
datasets including large baseline light field images demonstrate the
significant superiority of our method when compared with state-of-the-art ones,
i.e., our method improves the PSNR of the second best method up to 2 dB in
average, while saves the execution time 48. In addition, our method
preserves the light field parallax structure better.Comment: This paper was accepted by AAAI 202
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