Neural-network-aided design for image processing

Vitsnudel, I.; Ginosar, Ran; Zeevi, Yehoshua Y.

doi:10.1117/12.50370

Cited by 6 publications

(2 citation statements)

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“…We elaborate an approach suitable for SR based on a single image, similarly to [32]. Instead of using a sequence of video frames or multiple exposure, we exploit the properties common to a wide range of natural images.…”

Section: B Proposed Scheme: Single Image Srmentioning

confidence: 99%

Forward-and-backward diffusion processes for adaptive image enhancement and denoising

Gilboa

Sochen

Zeevi

2002

IEEE Trans. on Image Process.

313

170

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Signal and image enhancement is considered in the context of a new type of diffusion process that simultaneously enhances, sharpens, and denoises images. The nonlinear diffusion coefficient is locally adjusted according to image features such as edges, textures, and moments. As such, it can switch the diffusion process from a forward to a backward (inverse) mode according to a given set of criteria. This results in a forward-and-backward (FAB) adaptive diffusion process that enhances features while locally denoising smoother segments of the signal or image. The proposed method, using the FAB process, is applied in a super-resolution scheme. The FAB method is further generalized for color processing via the Beltrami flow, by adaptively modifying the structure tensor that controls the nonlinear diffusion process. The proposed structure tensor is neither positive definite nor negative, and switches between these states according to image features. This results in a forward-and-backward diffusion flow where different regions of the image are either forward or backward diffused according to the local geometry within a neighborhood.

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Section: B Proposed Scheme: Single Image Srmentioning

confidence: 99%

Forward-and-backward diffusion processes for adaptive image enhancement and denoising

Gilboa

Sochen

Zeevi

2002

IEEE Trans. on Image Process.

313

170

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“…During the same cycle (11), neurons N1-N5 also com mence a new cycle of calculation and receive input packets (1)(2)(3)(4)(5) that belong to the next 25 inputs of data example C.…”

Section: Initializationmentioning

confidence: 99%

A self-learning digital neural network using wafer-scale LSI

Yasunaga

Masuda

Yagyu

et al. 1993

IEEE J. Solid-State Circuits

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A digital systolic neural network chip (DSNC)

Weinberg

Ginosar

Eighteenth Convention of Electrical and Electronics Engineers in Israel

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The DSNC chip implements one neural network layer having 25 fully inter-connected neurons. It is a digital, systolic, pipelined, integer based architecture. The chip is primarily designed for image processing tasks devised upon the basic BackPropagation neuralnetwork algorithm. Forward processing as well as learning are supported and carried out in parallel with each other. A network built using this chip can have unlimited number of layers when working in Forward mode, and up to three layers if learning is required. The topology of a network is user defined. A chip can handle up to 25 inputs in each input example. Input and output data are nine bits; Weights, Biases and their updated values are eight bits. A chip can process images of up to 550x550 pixels in real-time when processing neighborhoods of 25 pixels, and larger images when using smaller neighborhoods. The chip requires about two million transistors and 180 I/O pins.

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Neural-network-aided design for image processing

Cited by 6 publications

References 0 publications

Forward-and-backward diffusion processes for adaptive image enhancement and denoising

Forward-and-backward diffusion processes for adaptive image enhancement and denoising

A self-learning digital neural network using wafer-scale LSI

A digital systolic neural network chip (DSNC)

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