Hyperspectral Imaging - Revision history

Mhefeeda: /* Codes and Datasets */

2025-11-12T06:55:26Z

Codes and Datasets

Mhefeeda at 03:52, 6 November 2025

2025-11-06T03:52:22Z

Mhefeeda at 03:50, 6 November 2025

2025-11-06T03:50:33Z

Mhefeeda: /* Code and Datasets */

2025-11-06T03:48:16Z

Code and Datasets

Mhefeeda: /* Publications */

2025-11-06T03:47:19Z

Publications

Mhefeeda: /* Publications */

2025-11-06T03:36:39Z

Publications

Mhefeeda: /* Code and Datasets */

2025-11-06T03:30:28Z

Code and Datasets

Sza191 at 00:11, 5 November 2025

2025-11-05T00:11:58Z

Sza191 at 00:11, 5 November 2025

2025-11-05T00:11:19Z

Sza191 at 00:10, 5 November 2025

2025-11-05T00:10:18Z

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 == Codes and Datasets ==
 * [https://github.com/chercode/mobilyzer-imwut25 MobiLyzer: Fine-grained Mobile Liquid Analyzer]
 * [https://github.com/mariam-bebawy/glucosense-mobicom25 GlucoSense: Non-Invasive Glucose Monitoring using Mobile Devices]
 * [https://github.com/mobispectral/mobicom23_mobispectral MobiSpectral: Hyperspectral Imaging on Mobile Devices]

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 Hyperspectral imaging systems capture information in multiple wavelength bands across the electromagnetic spectrum. These bands provide substantial details based on the optical properties of the materials present in the captured scene. The high cost of hyperspectral cameras and their strict illumination requirements make the technology out of reach for end-user and small-scale commercial applications. We propose MobiSpectral, which turns a low-cost phone into a simple hyperspectral imaging system, without any changes in the hardware. We design deep learning models that take regular RGB images and near-infrared (NIR) signals (which are used for face identification on recent phones) and reconstruct multiple hyperspectral bands in the visible and NIR ranges of the spectrum. Our experimental results show that MobiSpectral produces accurate bands that are comparable to ones captured by actual hyperspectral cameras. The availability of hyperspectral bands that reveal hidden information enables the development of novel mobile applications that are not currently possible. To demonstrate the potential of MobiSpectral, we use it to identify organic solid foods, which is a challenging food fraud problem that is currently partially addressed by laborious, unscalable, and expensive processes. We collect large datasets in real environments under diverse illumination conditions to evaluate MobiSpectral. Our results show that MobiSpectral can identify organic foods, e.g., apples, tomatoes, kiwis, strawberries, and blueberries, with an accuracy of up to 94% from images taken by phones.
-%[[File:Mobispectral.png|thumb|center|700px|Overview of MobiSpectral]]
+<!--
 [[File:Mobispectral.png|thumb|center|700px|Overview of MobiSpectral]]

← Older revision		Revision as of 03:50, 6 November 2025
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	Hyperspectral imaging systems capture information in multiple wavelength bands across the electromagnetic spectrum. These bands provide substantial details based on the optical properties of the materials present in the captured scene. The high cost of hyperspectral cameras and their strict illumination requirements make the technology out of reach for end-user and small-scale commercial applications. We propose MobiSpectral, which turns a low-cost phone into a simple hyperspectral imaging system, without any changes in the hardware. We design deep learning models that take regular RGB images and near-infrared (NIR) signals (which are used for face identification on recent phones) and reconstruct multiple hyperspectral bands in the visible and NIR ranges of the spectrum. Our experimental results show that MobiSpectral produces accurate bands that are comparable to ones captured by actual hyperspectral cameras. The availability of hyperspectral bands that reveal hidden information enables the development of novel mobile applications that are not currently possible. To demonstrate the potential of MobiSpectral, we use it to identify organic solid foods, which is a challenging food fraud problem that is currently partially addressed by laborious, unscalable, and expensive processes. We collect large datasets in real environments under diverse illumination conditions to evaluate MobiSpectral. Our results show that MobiSpectral can identify organic foods, e.g., apples, tomatoes, kiwis, strawberries, and blueberries, with an accuracy of up to 94% from images taken by phones.		Hyperspectral imaging systems capture information in multiple wavelength bands across the electromagnetic spectrum. These bands provide substantial details based on the optical properties of the materials present in the captured scene. The high cost of hyperspectral cameras and their strict illumination requirements make the technology out of reach for end-user and small-scale commercial applications. We propose MobiSpectral, which turns a low-cost phone into a simple hyperspectral imaging system, without any changes in the hardware. We design deep learning models that take regular RGB images and near-infrared (NIR) signals (which are used for face identification on recent phones) and reconstruct multiple hyperspectral bands in the visible and NIR ranges of the spectrum. Our experimental results show that MobiSpectral produces accurate bands that are comparable to ones captured by actual hyperspectral cameras. The availability of hyperspectral bands that reveal hidden information enables the development of novel mobile applications that are not currently possible. To demonstrate the potential of MobiSpectral, we use it to identify organic solid foods, which is a challenging food fraud problem that is currently partially addressed by laborious, unscalable, and expensive processes. We collect large datasets in real environments under diverse illumination conditions to evaluate MobiSpectral. Our results show that MobiSpectral can identify organic foods, e.g., apples, tomatoes, kiwis, strawberries, and blueberries, with an accuracy of up to 94% from images taken by phones.

−	[[File:Mobispectral.png\|thumb\|center\|700px\|Overview of MobiSpectral]]	+	%[[File:Mobispectral.png\|thumb\|center\|700px\|Overview of MobiSpectral]]

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 * Mariam Bebawy
-== Code and Datasets ==
+== Codes and Datasets ==
 * [https://github.com/chercode/mobilyzer-imwut25 MobiLyzer: Fine-grained Mobile Liquid Analyzer]
 * [https://github.com/mariam-bebawy/glucosense-mobicom25 GlucoSense: Non-Invasive Glucose Monitoring using Mobile Devices]
 * [https://github.com/mobispectral/mobicom23_mobispectral MobiSpectral: Hyperspectral Imaging on Mobile Devices]
 * [https://github.com/pazadimo/HS_In_Diverse_Illuminations Enabling Hyperspectral Imaging in Diverse Illumination Conditions for Indoor Applications]
 * [https://github.com/nehasharma512/vein-visualization Hyperspectral Reconstruction from RGB Images for Vein Visualization]
 * [https://github.com/maarab-sfu/BQSETHI Band and Quality Selection for Efficient Transmission of Hyperspectral Images]

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 == Publications ==
-* S. Mirzaei, M. Bebawy, A. M. Sharafeldin, and M. Hefeeda, [https://doi.org/10.1145/3770678 MobiLyzer: Fine-grained Mobile Liquid Analyzer], Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., vol. 9, no. 4, Art. 201, Dec. 2025
+* S. Mirzaei, M. Bebawy, A. M. Sharafeldin, and M. Hefeeda, [https://www2.cs.sfu.ca/~mhefeeda/Papers/imwut25_MobiLyzer.pdf MobiLyzer: Fine-grained Mobile Liquid Analyzer], Proc. ACM Interactive Mobile, Wearable, and Ubiquitous Technologies (IMWUT'25), vol. 9, no. 4, Article 201, pp. 1--37, December. 2025.
 * M. Waseem, N. Sharma, M. Hefeeda, [https://www2.cs.sfu.ca/~mhefeeda/Papers/tmc25_RipeTrack.pdf RipeTrack: Assessing Fruit Ripeness and Remaining Lifetime using Smartphones], IEEE Transactions on Mobile Computing, pp. 1—16, Accepted, August 2025.
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 * N. Sharma, M. Waseem, S. Mirzaei, and M. Hefeeda, [https://www2.cs.sfu.ca/~mhefeeda/Papers/mobiCom23_MobiSpectral.pdf MobiSpectral: Hyperspectral Imaging on Mobile Devices], In Proc. of ACM Conference on Mobile Computing and Networking (MobiCom'23), Madrid, Spain, October 2023. (Artifacts Evaluated and Functional)
 * P.  Moghadam, N. Sharma, M. Hefeeda, [https://www2.cs.sfu.ca/~mhefeeda/Papers/mmsys21_HS_illumination.pdf Enabling Hyperspectral Imaging in Diverse Illumination Conditions for Indoor Applications], In Proc. of ACM Multimedia Systems Conference (MMSys’21), Istanbul, Turkey, September 2021. (Artifacts Evaluated and Functional)

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 * M. Waseem, N. Sharma, M. Hefeeda, [https://www2.cs.sfu.ca/~mhefeeda/Papers/tmc25_RipeTrack.pdf RipeTrack: Assessing Fruit Ripeness and Remaining Lifetime using Smartphones], IEEE Transactions on Mobile Computing, pp. 1—16, Accepted, August 2025.
-* N. Sharma, M. Bebawy, Y. Ng, and M. Hefeeda, [https://www2.cs.sfu.ca/~mhefeeda/Papers/mobiCom25_GlucoSense.pdf GlucoSense: Non-Invasive Glucose Monitoring using Mobile Devices], In Proc. of ACM Conference on Mobile Computing and Networking (MobiCom'25), Hong Kong, China, November 2025.
+* N. Sharma, M. Bebawy, Y. Ng, and M. Hefeeda, [https://www2.cs.sfu.ca/~mhefeeda/Papers/mobicom25_GlucoSense.pdf GlucoSense: Non-Invasive Glucose Monitoring using Mobile Devices], In Proc. of ACM Conference on Mobile Computing and Networking (MobiCom'25), Hong Kong, China, November 2025.
 * N. Sharma, M. Waseem, S. Mirzaei, and M. Hefeeda, [https://www2.cs.sfu.ca/~mhefeeda/Papers/mobiCom23_MobiSpectral.pdf MobiSpectral: Hyperspectral Imaging on Mobile Devices], In Proc. of ACM Conference on Mobile Computing and Networking (MobiCom'23), Madrid, Spain, October 2023. (Artifacts Evaluated and Functional)

← Older revision		Revision as of 00:11, 5 November 2025
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	[[File:Mobispectral.png\|thumb\|center\|700px\|Overview of MobiSpectral]]		[[File:Mobispectral.png\|thumb\|center\|700px\|Overview of MobiSpectral]]
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	'''MobiLyzer: Fine-grained Mobile Liquid Analyzer'''		'''MobiLyzer: Fine-grained Mobile Liquid Analyzer'''

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 Most current methods for liquid analysis and fraud detection rely on expensive tools and controlled lab environments, making them inaccessible to lay users. We present MobiLyzer, a mobile system that enables fine-grained liquid analysis on unmodified commodity smartphones in realistic environments such as homes and grocery stores. MobiLyzer conducts spectral analysis of liquids based on how their chemical components reflect different wavelengths. Conducting spectral analysis of liquids on smartphones, however, is challenging due to the limited sensing capabilities of smartphones and the heterogeneity in their camera designs. This is further complicated by the uncontrolled nature of ambient illumination and the diversity in liquid containers. The ambient illumination, for example, introduces distortions in measured spectra, and liquid containers cause specular reflections that degrade accuracy. To address these challenges, MobiLyzer utilizes RGB images captured by regular smartphone cameras. It then introduces intrinsic decomposition ideas to mitigate the effects of illumination and interference from liquid containers. It further leverages the near-infrared (NIR) sensors on smartphones to collect complementary signals in the NIR spectral range, partially mitigating the limited sensing capabilities of smartphones. It finally presents a new machine-learning model that reconstructs the entire spectrum in the visible and NIR ranges using the captured RGB and NIR images, which enables fine-grained spectral analysis of liquids on smartphones without the need for expensive equipment. Unlike prior models, the presented spectral reconstruction model preserves the original RGB colors during reconstruction, which is critical for liquid analysis since many liquids differ only in subtle spectral cues. We demonstrate the accuracy and robustness of MobiLyzer through extensive experiments with multiple liquids, four different smartphones, and seven illumination sources. Our results show, for example, that MobiLyzer can accurately detect adulteration with small ratios, identify quality grades of the same liquid (e.g., refined vs. extra virgin olive oil), differentiate the country of origin of oils (e.g., olive oil from Italy versus USA), and analyze the concentration of materials in liquids (e.g., protein concentration in urine for early detection of kidney diseases).
-[[File:Overview.pngthumb|center|700px|Overview of MobiLyzer]]
+[[File:Overview.png|thumb|center|700px|Overview of MobiLyzer]]
 == People ==