Ternary Optical Computer: An Overview and Recent Developments

Wang, Hongjian; Ouyang, Shan; Shen, Yunfu; Chen, Xunlei

doi:10.1109/paap54281.2021.9720446

Cited by 2 publications

(1 citation statement)

References 32 publications

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“…The development of emerging technologies such as optical computing [12] and deep learning provides new solutions for image deblurring. Optical computing techniques can accelerate image processing and computation by exploiting the nonlinear properties of optical devices, which can be operated directly in the optical domain [13].…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Cyclic Image Deblurring Based on PVC-Resnet

et al. 2023

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Aiming at the non-uniform blurring of image caused by optical system defects or external interference factors, such as camera shake, out-of-focus, and fast movement of object, a multi-scale cyclic image deblurring model based on a parallel void convolution-Resnet (PVC-Resnet) is proposed in this paper, in which a multi-scale recurrent network architecture and a coarse-to-fine strategy are used to restore blurred images. The backbone network is built based on Unet codec architecture, where a PVC-Resnet module designed by combinations of parallel dilated convolution and residual network is constructed in the encoder of the backbone network. The convolution receptive field is expanded with parallel dilated convolution to extract richer global features. Besides, a multi-scale feature extraction module is designed to extract the shallow features of different scale targets in blurred images, and then the extracted features are sent to the backbone network for feature refinement. The SSIM loss function and the L1 loss function are combined to construct the SSIM-L1 joint loss function for the optimization of the overall network to ensure that the image restoration at different stages can be optimized. The experimental results show that the average peak signal-to-noise ratio (PSNR) of the proposed model on different data sets is as high as 32.84 dB, and the structural similarity (SSIM) reaches 0.9235. and statistical structural similarity (Stat-SSIM) of 0.9249 on different datasets. Compared with other methods, the deblurred images generated by this method are superior to the methods proposed by Nah et al., Kupyn et al. and Cho S J et al., especially on the calibration board data set. The model proposed in this paper applies parallel dilated convolution and SSIM-L1 joint loss function to improve the performance of the network so that the edge and texture details of the restored image are clearer.

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Section: Introductionmentioning

confidence: 99%

Multi-Scale Cyclic Image Deblurring Based on PVC-Resnet

et al. 2023

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Research on Multiplication Routine Based on Reconfigurable Four-Valued Logic Processor

Liao,

Li,

et al. 2024

Electronics

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Despite the indispensable role of traditional electronic computers in modern society, their limitations in parallel processing capabilities, bit-width constraints, and processor bit-width are becoming increasingly apparent, especially when handling large-scale datasets and complex computational tasks. Although hardware technology and algorithm optimization continue to advance, the arithmetic units of traditional computers—adders—remain constrained by carry delay and bit-width limitations. This bottleneck is particularly pronounced in multiplication operations, mainly when adders are used for partial product accumulation. However, since 2018, the emergence of a new type of Reconfigurable Four-Valued Logic Electronic Processor (RFLEP) has provided a potential solution to these traditional limitations. With its large processor bit-width, flexible bit grouping capabilities, and dynamic hardware function reconfiguration features, this processor has brought revolutionary changes to the field of computing. In this context, this paper proposes and implements a Reconfigurable Four-Valued Logic Multiplication Routine (RFLMR) tailored explicitly for the RFLEP. The RFLMR utilizes the Modified Signed-Digit (MSD) representation method in multi-valued logic combined with the M transformation in four-valued logic to generate partial products. These partial products are then efficiently summed in parallel using the JW-MSD parallel adder, achieving the rapid execution of multiplication operations. Experimental results demonstrate that the multiplication routine based on the RFLEP performs multiplication operations accurately and meets theoretical expectations regarding implementation efficiency and performance. This research not only provides new ideas for developing next-generation high-performance computing systems but also paves the way for exploring more efficient and powerful computing models, heralding a profound transformation in future computing technology.

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Ternary Optical Computer: An Overview and Recent Developments

Cited by 2 publications

References 32 publications

Multi-Scale Cyclic Image Deblurring Based on PVC-Resnet

Multi-Scale Cyclic Image Deblurring Based on PVC-Resnet

Research on Multiplication Routine Based on Reconfigurable Four-Valued Logic Processor

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