Local tone mapping algorithm and hardware implementation

Yang, Jie; Horé, Alain; Yadid-Pecht, Orly

doi:10.1049/el.2017.3227

Cited by 9 publications

(7 citation statements)

References 9 publications

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“…The horizontal cells stage is used to adjust the brightness of the input image with the 15 × 15 convolution. The 15 × 15 convolution is shown in Equation (1). Where the * represents the convolution operation, x input image (x, y) represent the input image, g(x, y; σ n (x, y)) represents Gaussian convolution filters that is determined by the value (i.e., brightness) range of the central pixel.…”

Section: Biological Retina Inspired Tone Mapping Algorithmmentioning

confidence: 99%

“…The selection of the Gaussian convolution filters is determined by Table 1, where m represents mean value and s represents standard deviation and sigma is a parameter defining the max coupling strength of horizontal cells, which is experimentally set to be 1.0 in this work. There are in total 4 Gaussian convolution filters (1,2,3,4), corresponding to the n of g(x, y; σ n (x, y)) in Equation (1), where n ∈ {1, 2, 3, 4}.…”

Section: Biological Retina Inspired Tone Mapping Algorithmmentioning

confidence: 99%

“…As one of the major image enhancement approaches, tone mapping has been widely used for recovering the image details in the dark or over-exposure areas in the high dynamic range images, by mapping the original dynamic range to a proper dynamic range. In the past, many tone mapping algorithms have been proposed [ 1 , 2 , 3 ]. Compared to general tone mapping algorithms, biological retina inspired tone mapping algorithms employ the processing mechanisms of the biological visual system that render the details in the dark areas in a more natural way.…”

Section: Introductionmentioning

confidence: 99%

“…In the past, different FPGA-based tone mapping processors have been proposed. Reference [ 1 ] proposed a tone mapping processor using a local tone mapping algorithm. It adopts different compression levels for each pixel according to the local pixel statistical characteristics.…”

Section: Introductionmentioning

confidence: 99%

“…In the past, different FPGA-based tone mapping processors have been proposed. Reference [1] proposed a tone mapping processor using a local tone mapping algorithm.…”

Section: Introductionmentioning

confidence: 99%

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A Biological Retina Inspired Tone Mapping Processor for High-Speed and Energy-Efficient Image Enhancement

Xiang

Liu

Que

et al. 2020

Sensors

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In this work, a biological retina inspired tone mapping processor for high-speed and energy-efficient image enhancement has been proposed. To achieve high throughput and high energy efficiency, several hardware design techniques have been proposed, including data partition based parallel processing with S-shape sliding, adjacent frame feature sharing, multi-layer convolution pipelining, and convolution filter compression with zero skipping convolution. Implemented on a Xilinx’s Virtex7 FPGA, the proposed design achieves a high throughput of 189 frames per second for 1024 × 768 RGB images while consuming 819 mW. Compared with several state-of-the-art tone mapping processors, the proposed design shows higher throughput and energy efficiency. It is suitable for high-speed and energy-constrained image enhancement applications.

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Section: Biological Retina Inspired Tone Mapping Algorithmmentioning

confidence: 99%

Section: Biological Retina Inspired Tone Mapping Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In the past, different FPGA-based tone mapping processors have been proposed. Reference [1] proposed a tone mapping processor using a local tone mapping algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Biological Retina Inspired Tone Mapping Processor for High-Speed and Energy-Efficient Image Enhancement

Xiang

Liu

Que

et al. 2020

Sensors

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Emerging Technologies in Multi‐Material Bioprinting

et al. 2021

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organ-specific tissues, [14] patient-specific grafts, [15] tissue model engineering, [16] and drug screening. [17] The most frequently used technologies for 3D bioprinting include nozzle-based and laser/light-based techniques. Extrusion [18] and inkjet [19] are arguably the most common modalities of nozzle-based bioprinting at the moment, while laser-induced forward transfer (LIFT) [20] and vat-photopolymerization [21] are the two frequently used for laser/lightbased 3D bioprinting. [22] Extrusion bioprinting involves fabrication on a bioprinting platform using bioinks extruded from one or several nozzle(s) (Figure 1A). The extrusion process may be pressure-controlled, with the bioink entrained by means of pneumatic actuation, or flow rate-controlled with the bioink forced by mechanical impulses through syringes. [23] Solidification of the bioprinted structures as they are delivered is obtained through physical, chemical, or photo-crosslinking. [24] Extrusion bioprinting is relatively inexpensive, straightforward, and convenient. It has been embodied, for example, within handheld and portable devices. [25][26][27][28][29] But such convenience must be traded off against significant challenges. Nozzle extrusion necessarily entails a high level of shear stress near the fluidic channel walls. Excessive shear, particularly in high-viscosity bioinks, jeopardizes cell viability. [30] High-resolution bioprinting often requires small-diameter nozzles. The greater shear required imposes a limit on bioink flow rate and throughput. This problem may be addressed through the use of printable shearthinning biomaterials, [31] for which the viscosity decreases under shear stress. Yet, shear-thinning bioink materials that meet all design requirements are not always available. Further notable challenges of extrusion methods include difficulties in finding stable in situ crosslinking methods for nonshear-thinning bioinks, as well as low printing resolution [32] in relation to other methods, and complications in the fabrication of freestanding constructs. [33,34] These shortcomings have spurred many enhancements, notably co-axial/core-shell bioprinting, [35] described in Section 3.1.3, and embedded bioprinting, [36] in Section 3.1.4.Inkjet bioprinting (Figure 1B), similar to home/office inkjet printing, delivers small droplets of bioink to a substrate and can produce high-resolution voxelated constructs. [37] Unlike the extrusion method, where shear-thinning bioinks with a broad range of viscosities can be used, inkjet bioprinters are mainly designed to work with low-viscosity bioinks. [38] The deposition of droplets/voxels is controlled either by thermal, piezoelectric, Bioprinting, within the emerging field of biofabrication, aims at the fabrication of functional biomimetic constructs. Different 3D bioprinting techniques have been adapted to bioprint cell-laden bioinks. However, single-material bioprinting techniques oftentimes fail to reproduce the complex compositions and diversity of native tissues. Multi-material bioprinting as...

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