Video denoising for raw image has always been the difficulty of camera imageprocessing. On the one hand, image denoising performance largely determines the image quality;moreover, denoising effect in raw image will a...
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Video denoising for raw image has always been the difficulty of camera imageprocessing. On the one hand, image denoising performance largely determines the image quality;moreover, denoising effect in raw image will affect the accuracy of the following operations of ISP processing flow. On the other hand, compared with image, video has motion information in time sequence;thus, motion estimation which is complex and computationally expensive is needed in video denoising. In view of the above problems, this paper proposes a video denoising algorithm for raw image, performing multiple cascading processing stages on raw-RGB image based on convolutional neural network, and carries out implicit motion estimation in the network. The denoising performance is far superior to that of traditional algorithms with minimal computation and bandwidth, and has computational advantages compared with most deep learning algorithms.
Deep learning algorithms are robust to a small amount of noise in the input image. Traditionally, image signal processors (ISP) are used with the CMOS image sensor (CIS) to enhance image quality which consume addition...
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ISBN:
(纸本)9798350387186;9798350387179
Deep learning algorithms are robust to a small amount of noise in the input image. Traditionally, image signal processors (ISP) are used with the CMOS image sensor (CIS) to enhance image quality which consume additional energy and latency. Here, we evaluate an ISP-less CIS architecture in the presence of noise and other in-pixel circuit non-idealities for the autonomous driving application. By integrating the in-pixel processing circuits to CIS, we filter out the redundant frames and only pass the critical bit information downstream to the backend processor. Such in-pixel processing does not allow ISP operations to be applied to the captured raw image. To reflect these limitations, we model and apply circuit non-idealities to the regenerated artificial raw images as an input to the QDTrack network for multi-object tracking. We evaluate the accuracy loss on the BDD100K dataset and examine its sensitivity on each of the imageprocessing steps. We observe an overall accuracy drop of less than 1.2% in Identification F1-score (IDF1) and 2.1% in Multi-Object Tracking Accuracy (MOTA), suggesting that an ISP-less in-pixel processing circuit is feasible to reject 40% redundant frames directly on CIS.
Embedded systems typically require the transmission of significant amounts of data to small-scale CPUs for applications such as radar signal processing, imageprocessing, and embedded AI. Ensuring data integrity durin...
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ISBN:
(纸本)9798350377217;9798350377200
Embedded systems typically require the transmission of significant amounts of data to small-scale CPUs for applications such as radar signal processing, imageprocessing, and embedded AI. Ensuring data integrity during transmission is typically managed using Cyclic Redundancy Check (CRC) algorithms. However, achieving real-time CRC calculation and data storage poses challenges, often necessitating large FIFO memories and multiple clock domains. These additional resources involve a greater hardware complexity. This paper presents an approach aimed at synchronizing the CPU frequency with data transmission. This enables having a single clock domain and a reduction of power consumption. Using hardware/software co-design, it is possible to achieve real-time data storage and CRC calculation without data loss and with a low power consumption.
Division is one of the most commonly sort after algorithm for performing imageprocessing operations such as normalization, filtering, enhancement, deconvolution etc. Hence, the design of efficient division algorithm ...
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Medical image segmentation plays a pivotal role in computer-aided diagnosis by facilitating the extraction of essential features necessary for disease detection and treatment strategies. The continuous progress in ima...
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This paper investigates classical imageprocessing techniques and unsupervised deep learning algorithms for segmenting images with high variance for an under researched industrial problem, focusing on beam burns gener...
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Watermelon is a commonly cultivated fruit worldwide, especially in Southeast Asia. As one of the top exports in Asia, the commercialization of watermelon has its market. Its consumer appeal makes it one of the most so...
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ISBN:
(纸本)9798350372113;9798350372106
Watermelon is a commonly cultivated fruit worldwide, especially in Southeast Asia. As one of the top exports in Asia, the commercialization of watermelon has its market. Its consumer appeal makes it one of the most sought-after fruits globally. With the watermelon fruit consisting of different varieties, consumers usually have difficulty in classifying watermelons due to their similar external appearances, especially when labeled under the same name. This study is conducted to implement a system that detects and classifies three red watermelon varieties, Red Export, Orchid Sweet, and Dixie Queen, with imageprocessingalgorithms. The researchers utilized Canny Edge Detection for the dataset's preprocessing phase and Convolutional Neural Network (CNN) for its classification. The Raspberry Pi is also applied to this study. Moreover, the researchers created and collected their datasets for testing and validation data. The model used in this study has acquired 84.71% overall accuracy.
The "Residual-to-Residual DNN series for high-Dynamic range imaging" (R2D2) approach was recently introduced for Radio-Interferometric (RI) imaging in astronomy. R2D2's reconstruction is formed as a seri...
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ISBN:
(纸本)9789464593617;9798331519773
The "Residual-to-Residual DNN series for high-Dynamic range imaging" (R2D2) approach was recently introduced for Radio-Interferometric (RI) imaging in astronomy. R2D2's reconstruction is formed as a series of residual images, iteratively estimated as outputs of Deep Neural Networks (DNNs) taking the previous iteration's image estimate and associated data residual as inputs. In this work, we investigate the robustness of the R2D2 image estimation process, by studying the uncertainty associated with its series of learned models. Adopting an ensemble averaging approach, multiple series can be trained, arising from different random DNN initializations of the training process at each iteration. The resulting multiple R2D2 instances can also be leveraged to generate "R2D2 samples", from which empirical mean and standard deviation endow the algorithm with a joint estimation and uncertainty quantification functionality. Focusing on RI imaging, and adopting a telescope-specific approach, multiple R2D2 instances were trained to encompass the most general observation setting of the Very Large Array (VLA). Simulations and real-data experiments confirm that: (i) R2D2's image estimation capability is superior to that of the state-of-the-art algorithms;(ii) its ultra-fast reconstruction capability (arising from series with only few DNNs) makes the computation of multiple reconstruction samples and of uncertainty maps practical even at large image dimension;(iii) it is characterized by a very low model uncertainty.
This article explores the application of deep learning (DL) algorithms in power system load forecasting. With the continuous advancement of the construction of new power systems, traditional load forecasting models de...
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The development of computer vision systems stimulates the development of various applications in the field of image recognition. Methods and algorithms for image recognition in document processingsystems play a cruci...
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