A Review of an Old Dilemma: Demosaicking First, or Denoising First?
- URL: http://arxiv.org/abs/2004.11577v1
- Date: Fri, 24 Apr 2020 07:32:17 GMT
- Title: A Review of an Old Dilemma: Demosaicking First, or Denoising First?
- Authors: Qiyu Jin and Gabriele Facciolo and Jean-Michel Morel
- Abstract summary: Denoising and demosaicking aim at reconstructing a full color image from a noisy color filter array (CFA) image.
In this paper, we review the main variants of these strategies and carry-out an extensive evaluation to find the best way to reconstruct full color images from a noisy mosaic.
- Score: 16.328866317851183
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Image denoising and demosaicking are the most important early stages in
digital camera pipelines. They constitute a severely ill-posed problem that
aims at reconstructing a full color image from a noisy color filter array (CFA)
image. In most of the literature, denoising and demosaicking are treated as two
independent problems, without considering their interaction, or asking which
should be applied first. Several recent works have started addressing them
jointly in works that involve heavy weight CNNs, thus incompatible with low
power portable imaging devices. Hence, the question of how to combine denoising
and demosaicking to reconstruct full color images remains very relevant: Is
denoising to be applied first, or should that be demosaicking first? In this
paper, we review the main variants of these strategies and carry-out an
extensive evaluation to find the best way to reconstruct full color images from
a noisy mosaic. We conclude that demosaicking should applied first, followed by
denoising. Yet we prove that this requires an adaptation of classic denoising
algorithms to demosaicked noise, which we justify and specify.
Related papers
- Combining Pre- and Post-Demosaicking Noise Removal for RAW Video [2.772895608190934]
Denoising is one of the fundamental steps of the processing pipeline that converts data captured by a camera sensor into a display-ready image or video.
We propose a self-similarity-based denoising scheme that weights both a pre- and a post-demosaicking denoiser for Bayer-patterned CFA video data.
We show that a balance between the two leads to better image quality, and we empirically find that higher noise levels benefit from a higher influence pre-demosaicking.
arXiv Detail & Related papers (2024-10-03T15:20:19Z) - How to Best Combine Demosaicing and Denoising? [16.921538543268216]
demosaicing and denoising play a critical role in the raw imaging pipeline.
Most demosaicing methods address the demosaicing of noise free images.
The real problem is to jointly denoise and demosaic noisy raw images.
arXiv Detail & Related papers (2024-08-13T07:23:53Z) - Stimulating the Diffusion Model for Image Denoising via Adaptive Embedding and Ensembling [56.506240377714754]
We present a novel strategy called the Diffusion Model for Image Denoising (DMID)
Our strategy includes an adaptive embedding method that embeds the noisy image into a pre-trained unconditional diffusion model.
Our DMID strategy achieves state-of-the-art performance on both distortion-based and perception-based metrics.
arXiv Detail & Related papers (2023-07-08T14:59:41Z) - Image Denoising: The Deep Learning Revolution and Beyond -- A Survey
Paper -- [19.648352957466983]
Image denoising is one of the oldest and most studied problems in image processing.
The penetration of deep learning into image processing brought a revolution to image denoising.
Recent transitions in the field of image denoising go far beyond the ability to design better denoisers.
arXiv Detail & Related papers (2023-01-09T14:16:40Z) - NBD-GAP: Non-Blind Image Deblurring Without Clean Target Images [79.33220095067749]
Large amounts of blurry-clean image pairs are required for training to achieve good performance.
Deep networks often fail to perform well when the blurry images and the blur kernels during testing are very different from the ones used during training.
arXiv Detail & Related papers (2022-09-20T06:21:11Z) - An Interpretation of Regularization by Denoising and its Application
with the Back-Projected Fidelity Term [55.34375605313277]
We show that the RED gradient can be seen as a (sub)gradient of a prior function--but taken at a denoised version of the point.
We propose to combine RED with the Back-Projection (BP) fidelity term rather than the common Least Squares (LS) term that is used in previous works.
arXiv Detail & Related papers (2021-01-27T18:45:35Z) - Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images [98.82804259905478]
We present Neighbor2Neighbor to train an effective image denoising model with only noisy images.
In detail, input and target used to train a network are images sub-sampled from the same noisy image.
A denoising network is trained on sub-sampled training pairs generated in the first stage, with a proposed regularizer as additional loss for better performance.
arXiv Detail & Related papers (2021-01-08T02:03:25Z) - Legacy Photo Editing with Learned Noise Prior [0.0]
We propose a noise prior learner NEGAN to simulate the noise distribution of real legacy photos using unpaired images.
We also create a large legacy photo dataset for learning noise prior.
Then, we propose an IEGAN framework performing image editing including joint denoising, inpainting and colorization based on the estimated noise prior.
arXiv Detail & Related papers (2020-11-23T10:18:01Z) - Joint Demosaicking and Denoising Benefits from a Two-stage Training
Strategy [28.69029171306052]
Image demosaicking and denoising are the first two key steps of the color image production pipeline.
In this paper, we address this problem by a hybrid machine learning method.
Our demosaicking algorithm, trained on noiseless images, combines a traditional method and a residual convolutional neural network (CNN)
arXiv Detail & Related papers (2020-09-14T05:23:58Z) - Unpaired Learning of Deep Image Denoising [80.34135728841382]
This paper presents a two-stage scheme by incorporating self-supervised learning and knowledge distillation.
For self-supervised learning, we suggest a dilated blind-spot network (D-BSN) to learn denoising solely from real noisy images.
Experiments show that our unpaired learning method performs favorably on both synthetic noisy images and real-world noisy photographs.
arXiv Detail & Related papers (2020-08-31T16:22:40Z) - Deep Learning on Image Denoising: An overview [92.07378559622889]
We offer a comparative study of deep techniques in image denoising.
We first classify the deep convolutional neural networks (CNNs) for additive white noisy images.
Next, we compare the state-of-the-art methods on public denoising datasets in terms of quantitative and qualitative analysis.
arXiv Detail & Related papers (2019-12-31T05:03:57Z)
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.