Shallow camera pipeline for night photography rendering
- URL: http://arxiv.org/abs/2204.08972v1
- Date: Tue, 19 Apr 2022 16:18:21 GMT
- Title: Shallow camera pipeline for night photography rendering
- Authors: Simone Zini, Claudio Rota, Marco Buzzelli, Simone Bianco and Raimondo
Schettini
- Abstract summary: We introduce a camera pipeline for rendering photographs in low light conditions, as part of the NTIRE2022 Night Photography Rendering challenge.
Our pipeline exploits a local light enhancer as a form of high dynamic range correction, followed by a global adjustment of the image histogram to prevent washed-out results.
The solution reached the fifth place in the competition, with a preference vote count comparable to those of other entries, based on deep convolutional neural networks.
- Score: 20.683831741296444
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We introduce a camera pipeline for rendering visually pleasing photographs in
low light conditions, as part of the NTIRE2022 Night Photography Rendering
challenge. Given the nature of the task, where the objective is verbally
defined by an expert photographer instead of relying on explicit ground truth
images, we design an handcrafted solution, characterized by a shallow structure
and by a low parameter count. Our pipeline exploits a local light enhancer as a
form of high dynamic range correction, followed by a global adjustment of the
image histogram to prevent washed-out results. We proportionally apply image
denoising to darker regions, where it is more easily perceived, without losing
details on brighter regions. The solution reached the fifth place in the
competition, with a preference vote count comparable to those of other entries,
based on deep convolutional neural networks. Code is available at
www.github.com/AvailableAfterAcceptance.
Related papers
- Camera Relocalization in Shadow-free Neural Radiance Fields [16.359064848532483]
Camera relocalization is a crucial problem in computer vision and robotics.
Recent advancements in neural radiance fields (NeRFs) have shown promise in photo-realistic images.
We propose a two-staged pipeline that normalizes images with varying lighting and shadow conditions to improve camera relocalization.
arXiv Detail & Related papers (2024-05-23T17:41:15Z) - LDM-ISP: Enhancing Neural ISP for Low Light with Latent Diffusion Models [54.93010869546011]
We propose to leverage the pre-trained latent diffusion model to perform the neural ISP for enhancing extremely low-light images.
Specifically, to tailor the pre-trained latent diffusion model to operate on the RAW domain, we train a set of lightweight taming modules.
We observe different roles of UNet denoising and decoder reconstruction in the latent diffusion model, which inspires us to decompose the low-light image enhancement task into latent-space low-frequency content generation and decoding-phase high-frequency detail maintenance.
arXiv Detail & Related papers (2023-12-02T04:31:51Z) - Dimma: Semi-supervised Low Light Image Enhancement with Adaptive Dimming [0.728258471592763]
Enhancing low-light images while maintaining natural colors is a challenging problem due to camera processing variations.
We propose Dimma, a semi-supervised approach that aligns with any camera by utilizing a small set of image pairs.
We achieve that by introducing a convolutional mixture density network that generates distorted colors of the scene based on the illumination differences.
arXiv Detail & Related papers (2023-10-14T17:59:46Z) - Enhancing Low-Light Images Using Infrared-Encoded Images [81.8710581927427]
Previous arts mainly focus on the low-light images captured in the visible spectrum using pixel-wise loss.
We propose a novel approach to increase the visibility of images captured under low-light environments by removing the in-camera infrared (IR) cut-off filter.
arXiv Detail & Related papers (2023-07-09T08:29:19Z) - Single Image LDR to HDR Conversion using Conditional Diffusion [18.466814193413487]
Digital imaging aims to replicate realistic scenes, but Low Dynamic Range (LDR) cameras cannot represent the wide dynamic range of real scenes.
This paper presents a deep learning-based approach for recovering intricate details from shadows and highlights.
We incorporate a deep-based autoencoder in our proposed framework to enhance the quality of the latent representation of LDR image used for conditioning.
arXiv Detail & Related papers (2023-07-06T07:19:47Z) - Seeing Through The Noisy Dark: Toward Real-world Low-Light Image
Enhancement and Denoising [125.56062454927755]
Real-world low-light environment usually suffer from lower visibility and heavier noise, due to insufficient light or hardware limitation.
We propose a novel end-to-end method termed Real-world Low-light Enhancement & Denoising Network (RLED-Net)
arXiv Detail & Related papers (2022-10-02T14:57:23Z) - Enhancing Low-Light Images in Real World via Cross-Image Disentanglement [58.754943762945864]
We propose a new low-light image enhancement dataset consisting of misaligned training images with real-world corruptions.
Our model achieves state-of-the-art performances on both the newly proposed dataset and other popular low-light datasets.
arXiv Detail & Related papers (2022-01-10T03:12:52Z) - Low-light Image Enhancement via Breaking Down the Darkness [8.707025631892202]
This paper presents a novel framework inspired by the divide-and-rule principle.
We propose to convert an image from the RGB space into a luminance-chrominance one.
An adjustable noise suppression network is designed to eliminate noise in the brightened luminance.
The enhanced luminance further serves as guidance for the chrominance mapper to generate realistic colors.
arXiv Detail & Related papers (2021-11-30T16:50:59Z) - Deep Denoising of Flash and No-Flash Pairs for Photography in Low-Light
Environments [51.74566709730618]
We introduce a neural network-based method to denoise pairs of images taken in quick succession, with and without a flash, in low-light environments.
Our goal is to produce a high-quality rendering of the scene that preserves the color and mood from the ambient illumination of the noisy no-flash image.
arXiv Detail & Related papers (2020-12-09T15:41:16Z) - 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) - Burst Denoising of Dark Images [19.85860245798819]
We propose a deep learning framework for obtaining clean and colorful RGB images from extremely dark raw images.
The backbone of our framework is a novel coarse-to-fine network architecture that generates high-quality outputs in a progressive manner.
Our experiments demonstrate that the proposed approach leads to perceptually more pleasing results than state-of-the-art methods.
arXiv Detail & Related papers (2020-03-17T17:17:36Z)
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.