Reconfigurable Processor for Energy-Efficient Computational Photography

Rithe, Rahul; Raina, Priyanka; Ickes, Nathan; Tenneti, Srikanth V.; Chandrakasan, Anantha P.

doi:10.1109/jssc.2013.2282614

Cited by 12 publications

(3 citation statements)

References 29 publications

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“…He used Principal Component Analysis technique and produced a reasonably better quality fused deburred image [11]. Rithe [12] demonstrated a scalable reconfigurable bilateral filtering processor on a chip. The 40nm CMOS chip processing 13 megapixels achieved significant energy reduction.…”

Section: Literature Surveymentioning

confidence: 99%

Pixel Optimization Using Iterative Pixel Compression Algorithm for Complementary Metal Oxide Semiconductor Image Sensors

Palani,

Alharbi,

Alshahrani

et al. 2023

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The research presents a unique approach to the iterative pixel compression method for pixel optimization by reducing noise with a motion-guided backdrop. Image resolution and precision are increased by using a complementary metal oxide semiconductor (CMOS) image sensor. Researchers offer a dispersed equivalent implementation of the Iterative Pixel Compression technique for CMOS image sensors in order to successfully handle the expanded data. The current frame is handled by the buffer circuit in the CMOS image sensor. The registered bank is related to subsequent frames. It consists of a collection of registers that retain information on the grey levels of the acquired pictures' pixels. The image DE noising signal process is applied to the input picture, which contains noise. The pixel averaging filter is used in image DE noising to enhance picture quality and produce a better estimate. Pixel ordering identifies misplaced areas of photos due to the use of an iterative pixel reduction method. It allocates the best existing pixel feasible. Peak signal-to-noise ratio (PSNR) assess the image's quality through and Mean Square Error (MSE). When compared to previous approaches, our results demonstrate a 2% improvement in PSNR and a 1% reduction in MSE.

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Section: Literature Surveymentioning

confidence: 99%

Pixel Optimization Using Iterative Pixel Compression Algorithm for Complementary Metal Oxide Semiconductor Image Sensors

Palani,

Alharbi,

Alshahrani

et al. 2023

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“…It is the focus of the computational imaging field to extract high-dimensional data from images and transform it into useful numerical or symbolic information that can be further processed, interpreted and used in decisionmaking [90]. With advances in deep learning with artificial neural networks, digital computers are now able to analyze images with a logic structure that is similar to how humans think [91,92]. However, there are many imaging applications that require high-throughput, real-time, and low power image processing for which digital electronics is not ideally suited.…”

Section: Open Challenges and Opportunities For Metasurfacesmentioning

confidence: 99%

The road to atomically thin metasurface optics

Brongersma

2020

Nanophotonics

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The development of flat optics has taken the world by storm. The initial mission was to try and replace conventional optical elements by thinner, lightweight equivalents. However, while developing this technology and learning about its strengths and limitations, researchers have identified a myriad of exciting new opportunities. It is therefore a great moment to explore where flat optics can really make a difference and what materials and building blocks are needed to make further progress. Building on its strengths, flat optics is bound to impact computational imaging, active wavefront manipulation, ultrafast spatiotemporal control of light, quantum communications, thermal emission management, novel display technologies, and sensing. In parallel with the development of flat optics, we have witnessed an incredible progress in the large-area synthesis and physical understanding of atomically thin, two-dimensional (2D) quantum materials. Given that these materials bring a wealth of unique physical properties and feature the same dimensionality as planar optical elements, they appear to have exactly what it takes to develop the next generation of high-performance flat optics.

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“…The most important goal of computational photography is to extend the depth of field and the field of view. The representative research interests of computational photography include areas such as the design of special optics, improvement of digital sensors, application of modern processors, development of light-field photography, and implementation of three-dimensional (3-D) imaging [3][4][5][6][7]. In the case of 3-D imaging, a multi-view imaging system with axially distributed stereo image sensing and ray back-projection has been proposed for object visualization of partially occluded [8].…”

mentioning

confidence: 99%

Depth-based computational photography

Liu

et al. 2015

Journal of Modern Optics

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A depth-based computational photography model is proposed for all-in-focus image capture. A decomposition function, a defocus matrix, and a depth matrix are introduced to construct the photography model. The original image acquired from a camera can be decomposed into several sub-images on the basis of depth information. The defocus matrix can be deduced from the depth matrix according to the sensor defocus geometry for a thin lens model. And the depth matrix is reconstructed using the axial binocular stereo vision algorithm. This photography model adopts an energy functional minimization method to acquire the sharpest image pieces separately. The implementation of the photography method is described in detail. Experimental results for an actual scene demonstrate that our model is effective. IntroductionThe development of photography has involved four main stages: pinhole photography, lens photography, digital photography, and computational photography [1]. Computational photography contributes to recording of a more plentiful visual experience, capturing information beyond just a simple set of pixels and making the recorded scene representation far more machine readable [2]. The most important goal of computational photography is to extend the depth of field and the field of view. The representative research interests of computational photography include areas such as the design of special optics, improvement of digital sensors, application of modern processors, development of light-field photography, and implementation of three-dimensional (3-D) imaging [3][4][5][6][7]. In the case of 3-D imaging, a multi-view imaging system with axially distributed stereo image sensing and ray back-projection has been proposed for object visualization of partially occluded [8]. A unifying framework has been presented to evaluate the lateral and axial resolution of N-ocular imaging systems under fixed resource constraints [9]. Apart from these studies, reconstructions of defocus and depth maps, which can be achieved using the defocus-from-depth method [10], are important issues in 3-D photography. This approach also describes the relationships among image defocus, sensor defocus, and scene defocus in a camera system.Here, we present a depth-based computational photography model, which is essentially a 3-D photography method, for extending the depth of field and reconstructing

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Reconfigurable Processor for Energy-Efficient Computational Photography

Cited by 12 publications

References 29 publications

Pixel Optimization Using Iterative Pixel Compression Algorithm for Complementary Metal Oxide Semiconductor Image Sensors

Pixel Optimization Using Iterative Pixel Compression Algorithm for Complementary Metal Oxide Semiconductor Image Sensors

The road to atomically thin metasurface optics

Depth-based computational photography

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