475 research outputs found
NDELS: A Novel Approach for Nighttime Dehazing, Low-Light Enhancement, and Light Suppression
This paper tackles the intricate challenge of improving the quality of
nighttime images under hazy and low-light conditions. Overcoming issues
including nonuniform illumination glows, texture blurring, glow effects, color
distortion, noise disturbance, and overall, low light have proven daunting.
Despite the inherent difficulties, this paper introduces a pioneering solution
named Nighttime Dehazing, Low-Light Enhancement, and Light Suppression (NDELS).
NDELS utilizes a unique network that combines three essential processes to
enhance visibility, brighten low-light regions, and effectively suppress glare
from bright light sources. In contrast to limited progress in nighttime
dehazing, unlike its daytime counterpart, NDELS presents a comprehensive and
innovative approach. The efficacy of NDELS is rigorously validated through
extensive comparisons with eight state-of-the-art algorithms across four
diverse datasets. Experimental results showcase the superior performance of our
method, demonstrating its outperformance in terms of overall image quality,
including color and edge enhancement. Quantitative (PSNR, SSIM) and qualitative
metrics (CLIPIQA, MANIQA, TRES), measure these results
Improving Lens Flare Removal with General Purpose Pipeline and Multiple Light Sources Recovery
When taking images against strong light sources, the resulting images often
contain heterogeneous flare artifacts. These artifacts can importantly affect
image visual quality and downstream computer vision tasks. While collecting
real data pairs of flare-corrupted/flare-free images for training flare removal
models is challenging, current methods utilize the direct-add approach to
synthesize data. However, these methods do not consider automatic exposure and
tone mapping in image signal processing pipeline (ISP), leading to the limited
generalization capability of deep models training using such data. Besides,
existing methods struggle to handle multiple light sources due to the different
sizes, shapes and illuminance of various light sources. In this paper, we
propose a solution to improve the performance of lens flare removal by
revisiting the ISP and remodeling the principle of automatic exposure in the
synthesis pipeline and design a more reliable light sources recovery strategy.
The new pipeline approaches realistic imaging by discriminating the local and
global illumination through convex combination, avoiding global illumination
shifting and local over-saturation. Our strategy for recovering multiple light
sources convexly averages the input and output of the neural network based on
illuminance levels, thereby avoiding the need for a hard threshold in
identifying light sources. We also contribute a new flare removal testing
dataset containing the flare-corrupted images captured by ten types of consumer
electronics. The dataset facilitates the verification of the generalization
capability of flare removal methods. Extensive experiments show that our
solution can effectively improve the performance of lens flare removal and push
the frontier toward more general situations.Comment: ICCV 202
Enhancing Visibility in Nighttime Haze Images Using Guided APSF and Gradient Adaptive Convolution
Visibility in hazy nighttime scenes is frequently reduced by multiple
factors, including low light, intense glow, light scattering, and the presence
of multicolored light sources. Existing nighttime dehazing methods often
struggle with handling glow or low-light conditions, resulting in either
excessively dark visuals or unsuppressed glow outputs. In this paper, we
enhance the visibility from a single nighttime haze image by suppressing glow
and enhancing low-light regions. To handle glow effects, our framework learns
from the rendered glow pairs. Specifically, a light source aware network is
proposed to detect light sources of night images, followed by the APSF (Angular
Point Spread Function)-guided glow rendering. Our framework is then trained on
the rendered images, resulting in glow suppression. Moreover, we utilize
gradient-adaptive convolution, to capture edges and textures in hazy scenes. By
leveraging extracted edges and textures, we enhance the contrast of the scene
without losing important structural details. To boost low-light intensity, our
network learns an attention map, then adjusted by gamma correction. This
attention has high values on low-light regions and low values on haze and glow
regions. Extensive evaluation on real nighttime haze images, demonstrates the
effectiveness of our method. Our experiments demonstrate that our method
achieves a PSNR of 30.38dB, outperforming state-of-the-art methods by 13 on
GTA5 nighttime haze dataset. Our data and code is available at:
\url{https://github.com/jinyeying/nighttime_dehaze}.Comment: Accepted to ACM'MM2023, https://github.com/jinyeying/nighttime_dehaz
- …