Demosaicing and denoising of RAW images are crucial steps in the image signal processing pipeline of modern digital cameras. As only a third of the color information required to produce a digital image is captured by ...
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ISBN:
(纸本)9798350344868;9798350344851
Demosaicing and denoising of RAW images are crucial steps in the image signal processing pipeline of modern digital cameras. As only a third of the color information required to produce a digital image is captured by the camera sensor, the process of demosaicing is inherently ill-posed. The presence of noise further exacerbates this problem. Performing these two steps sequentially may distort the content of the captured RAWimages and accumulate errors from one step to another. Recent deep neural-network-based approaches have shown the effectiveness of joint demosaicing and denoising to mitigate such challenges. However, these methods typically require a large number of training samples and do not generalize well to different types and intensities of noise. In this paper, we propose a novel joint demosaicing and denoising method, dubbed JDD-DoubleDIP, which operates directly on a single RAW image without requiring any training data. We validate the effectiveness of our method on two popular datasets-Kodak and McMaster-with various noises and noise intensities. The experimental results show that our method consistently outperforms other compared methods in terms of PSNR, SSIM, and qualitative visual perception.
作者:
Yi, WangSchool of Law
Shandong University of Technology Economic Law Shandong Zibo255000 China
This research focuses on constructing an efficient imageprocessing model, which is rooted in computer vision algorithms, to ameliorate image distortion and optimize visual display systems. The article initially discu...
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This study proposes a graphic visual art design system based on human-computer interaction and digital imageprocessing technology to address the problems of traditional graphic visual art design methods being time-co...
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With the democratization of acquisition systems, 3D meshes have established themselves as the most optimal modality for representing, analyzing, transmitting, editing and printing 3D contents. All these processes have...
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We propose a learning-based image restoration algorithm for a single decoded image with a high-quality foreground and an extremely degraded background for video coding for machines (VCM). First, we develop an encoder ...
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ISBN:
(纸本)9781728198354
We propose a learning-based image restoration algorithm for a single decoded image with a high-quality foreground and an extremely degraded background for video coding for machines (VCM). First, we develop an encoder that extracts multiscale features and learns latent vectors. Then, a background generator with style and feature fusion blocks generates guided features that contain the prior background information in the input image. Finally, the decoder restores the degraded background region by merging the image features from the encoder and prior background information from the generator. Experimental results show that the proposed algorithm achieves better performance than state-of-the-art algorithms.
Millimeter wave (mmWave) wireless communications are significant technologies that support Internet of Things (IoT) systems to achieve fast and stable data transmission, and the guarantee of its communication quality ...
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image denoising is a representative image restoration task in computer vision. Recent progress of image denoising from only noisy images has attracted much attention. Deep image prior (DIP) demonstrated successful ima...
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ISBN:
(纸本)9781728198354
image denoising is a representative image restoration task in computer vision. Recent progress of image denoising from only noisy images has attracted much attention. Deep image prior (DIP) demonstrated successful image denoising from only a noisy image by inductive bias of convolutional neural network architectures without any pre-training. The major challenge of DIP based image denoising is that DIP would completely recover the original noisy image unless applying early stopping. For early stopping without a ground-truth clean image, we propose to monitor JPEG file size of the recovered image during optimization as a proxy metric of noise levels in the recovered image. Our experiments show that the compressed image file size works as an effective metric for early stopping.
Traditional image coding standards are typically optimized with a focus on human perception, which conflicts with the fact that most of the images are now analyzed by machines. To enable a variety of downstream intell...
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Today's image quality estimation is widely dominated by learning-based approaches. The availability of annotated, i.e. rated, images is often a bottleneck in training datadriven visual quality models and hinders t...
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ISBN:
(纸本)9781728198354
Today's image quality estimation is widely dominated by learning-based approaches. The availability of annotated, i.e. rated, images is often a bottleneck in training datadriven visual quality models and hinders their generalization power. This paper proposed a novel pre-training scheme for learning-based quality estimation that does not rely on human-annotated datasets, but leverages synthetic fractal images. These images can be synthesized inexhaustibly and are inherently labeled during generation. We evaluate the pre-training strategy on a popular neural network-based quality model and show that the training effort can be reduced significantly, resulting in better final accuracy and faster convergence speed.
This paper addresses two key limitations in existing image Signal processing (ISP) approaches: the suboptimal performance in low-light conditions and the lack of trainability in traditional ISP methods. To tackle thes...
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ISBN:
(纸本)9798350344868;9798350344851
This paper addresses two key limitations in existing image Signal processing (ISP) approaches: the suboptimal performance in low-light conditions and the lack of trainability in traditional ISP methods. To tackle these issues, we propose a novel, trainable ISP framework that incorporates both the strengths of traditional ISP techniques and advanced MultiScale Retinex (MSR) algorithms for night-time enhancement. Our method consists of three primary components: an ISP-based Luminance Harmonization layer to initially optimize luminance levels in RAW data, a deep learning-based MSR layer for nuanced decomposition of image components, and a specialized enhancement layer for both precise, regionspecific luminance enhancement and color denoising. The proposed approach is validated through rigorous experiments on machine vision benchmarks and objective visual quality indicators. Our results demonstrate not only a significant improvement over existing methods but also robust adaptability under diverse lighting conditions. This work offers a versatile ISP framework with promising applications beyond its immediate scope.
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