Bio-Inspired Night Image Enhancement Based on Contrast Enhancement and
Denoising
- URL: http://arxiv.org/abs/2307.05447v1
- Date: Tue, 11 Jul 2023 17:22:22 GMT
- Title: Bio-Inspired Night Image Enhancement Based on Contrast Enhancement and
Denoising
- Authors: Xinyi Bai, Steffi Agino Priyanka, Hsiao-Jung Tung, and Yuankai Wang
- Abstract summary: Compared with the corresponding daytime image, nighttime image is characterized as low brightness, low contrast and high noise.
In this paper, a bio-inspired image enhancement algorithm is proposed to convert a low illuminance image to a brighter and clear one.
- Score: 0.13124513975412253
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Due to the low accuracy of object detection and recognition in many
intelligent surveillance systems at nighttime, the quality of night images is
crucial. Compared with the corresponding daytime image, nighttime image is
characterized as low brightness, low contrast and high noise. In this paper, a
bio-inspired image enhancement algorithm is proposed to convert a low
illuminance image to a brighter and clear one. Different from existing
bio-inspired algorithm, the proposed method doesn't use any training sequences,
we depend on a novel chain of contrast enhancement and denoising algorithms
without using any forms of recursive functions. Our method can largely improve
the brightness and contrast of night images, besides, suppress noise. Then we
implement on real experiment, and simulation experiment to test our algorithms.
Both results show the advantages of proposed algorithm over contrast pair,
Meylan and Retinex.
Related papers
- Self-Reference Deep Adaptive Curve Estimation for Low-Light Image
Enhancement [7.253235412867934]
We propose a 2-stage low-light image enhancement method called Self-Reference Deep Adaptive Curve Estimation (Self-DACE)
In the first stage, we present an intuitive, lightweight, fast, and unsupervised luminance enhancement algorithm.
We also propose a new loss function with a simplified physical model designed to preserve natural images' color, structure, and fidelity.
arXiv Detail & Related papers (2023-08-16T07:57:35Z) - Advancing Unsupervised Low-light Image Enhancement: Noise Estimation, Illumination Interpolation, and Self-Regulation [55.07472635587852]
Low-Light Image Enhancement (LLIE) techniques have made notable advancements in preserving image details and enhancing contrast.
These approaches encounter persistent challenges in efficiently mitigating dynamic noise and accommodating diverse low-light scenarios.
We first propose a method for estimating the noise level in low light images in a quick and accurate way.
We then devise a Learnable Illumination Interpolator (LII) to satisfy general constraints between illumination and input.
arXiv Detail & Related papers (2023-05-17T13:56:48Z) - When the Sun Goes Down: Repairing Photometric Losses for All-Day Depth
Estimation [47.617222712429026]
We show how to use a combination of three techniques to allow the existing photometric losses to work for both day and nighttime images.
First, we introduce a per-pixel neural intensity transformation to compensate for the light changes that occur between successive frames.
Second, we predict a per-pixel residual flow map that we use to correct the reprojection correspondences induced by the estimated ego-motion and depth.
arXiv Detail & Related papers (2022-06-28T09:29:55Z) - Details Preserving Deep Collaborative Filtering-Based Method for Image
Denoising [3.4176234391973512]
We propose a deep collaborative filtering-based (Deep-CoFiB) algorithm for image denoising.
This algorithm performs collaborative denoising of image patches in the sparse domain using a set of optimized neural network models.
Extensive experiments show that the DeepCoFiB performed quantitatively (in terms of PSNR and SSIM) better than many of the state-of-the-art denoising algorithms.
arXiv Detail & Related papers (2021-07-11T19:02:36Z) - Context-Aware Image Denoising with Auto-Threshold Canny Edge Detection
to Suppress Adversarial Perturbation [0.8021197489470756]
This paper presents a novel context-aware image denoising algorithm.
It combines an adaptive image smoothing technique and color reduction techniques to remove perturbation from adversarial images.
Our results show that the proposed approach reduces adversarial perturbation in adversarial attacks and increases the robustness of the deep convolutional neural network models.
arXiv Detail & Related papers (2021-01-14T19:15:28Z) - Nighttime Dehazing with a Synthetic Benchmark [147.21955799938115]
We propose a novel synthetic method called 3R to simulate nighttime hazy images from daytime clear images.
We generate realistic nighttime hazy images by sampling real-world light colors from a prior empirical distribution.
Experiment results demonstrate their superiority over state-of-the-art methods in terms of both image quality and runtime.
arXiv Detail & Related papers (2020-08-10T02:16:46Z) - Depth image denoising using nuclear norm and learning graph model [107.51199787840066]
Group-based image restoration methods are more effective in gathering the similarity among patches.
For each patch, we find and group the most similar patches within a searching window.
The proposed method is superior to other current state-of-the-art denoising methods in both subjective and objective criterion.
arXiv Detail & Related papers (2020-08-09T15:12:16Z) - Single Image Brightening via Multi-Scale Exposure Fusion with Hybrid
Learning [48.890709236564945]
A small ISO and a small exposure time are usually used to capture an image in the back or low light conditions.
In this paper, a single image brightening algorithm is introduced to brighten such an image.
The proposed algorithm includes a unique hybrid learning framework to generate two virtual images with large exposure times.
arXiv Detail & Related papers (2020-07-04T08:23:07Z) - Deep Bilateral Retinex for Low-Light Image Enhancement [96.15991198417552]
Low-light images suffer from poor visibility caused by low contrast, color distortion and measurement noise.
This paper proposes a deep learning method for low-light image enhancement with a particular focus on handling the measurement noise.
The proposed method is very competitive to the state-of-the-art methods, and has significant advantage over others when processing images captured in extremely low lighting conditions.
arXiv Detail & Related papers (2020-07-04T06:26:44Z) - Learning an Adaptive Model for Extreme Low-light Raw Image Processing [5.706764509663774]
We propose an adaptive low-light raw image enhancement network to improve image quality.
The proposed method has the lowest Noise Level Estimation (NLE) score compared with the state-of-the-art low-light algorithms.
The potential application in video processing is briefly discussed.
arXiv Detail & Related papers (2020-04-22T09:01:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.