PetykiewiczBuckley - Revision history

imported>Psych2012: /* Results */

2012-03-21T03:46:22Z

Results

← Older revision		Revision as of 03:46, 21 March 2012
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			The results of the viewer study (described in methods).
	[[File:ViewStudyTable.jpg\|500px\|center]]		[[File:ViewStudyTable.jpg\|500px\|center]]

imported>Psych2012: /* Results */

2012-03-21T03:45:46Z

Results

← Older revision		Revision as of 03:45, 21 March 2012
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	Images taken from dataset and visible image was converted from sRGB to CIELAB for dehazing. The red dehazed image was dehazed with X-.192Z to eliminate part of X that extends into the blue. While some more detail can be seen in the NIR dehazed image compared to red dehazed image, examination of the images show that the mountains and trees in the background have become far bluer.		Images taken from dataset and visible image was converted from sRGB to CIELAB for dehazing. The red dehazed image was dehazed with X-.192Z to eliminate part of X that extends into the blue. While some more detail can be seen in the NIR dehazed image compared to red dehazed image, examination of the images show that the mountains and trees in the background have become far bluer and even white in the bottom image.

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imported>Psych2012: /* Results */

2012-03-21T03:44:56Z

Results

← Older revision		Revision as of 03:44, 21 March 2012
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	\|}		\|}

			Images taken from dataset and visible image was converted from sRGB to CIELAB for dehazing. The red dehazed image was dehazed with X-.192Z to eliminate part of X that extends into the blue. While some more detail can be seen in the NIR dehazed image compared to red dehazed image, examination of the images show that the mountains and trees in the background have become far bluer.

	{\| cellpadding="2" style="border: 1px solid darkgray; margin:auto;"		{\| cellpadding="2" style="border: 1px solid darkgray; margin:auto;"

imported>Psych2012: /* Methods */

2012-03-21T03:25:23Z

Methods

← Older revision		Revision as of 03:25, 21 March 2012
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	=Methods=		=Methods=
	We started with the hyperspectral panorama taken from the dish. Initially, we converted (scenes from) this data to the CIEXYZ space by interpolating the XYZ curves from ISET at the wavelengths at which the hyperspectral data was taken, and using these curves to weight the spectral data (see attached code). To view the image, we then converted the XYZ data to sRGB and used the Matlab image processing toolbox to display it. We later used sensor spectral responses (from the lecture notes, Nikon, QImaging and Kodak) to convert to RGB. The dehazing was done using the L* channel in CIELAB space (although we also investigated using a linear luminance channel). ~~The panorama which we received in hyperspectral data is shown converted to sRGB below.~~		We started with the hyperspectral panorama taken from the dish. Initially, we converted (scenes from) this data to the CIEXYZ space by interpolating the XYZ curves from ISET at the wavelengths at which the hyperspectral data was taken, and using these curves to weight the spectral data (see attached code). To view the image, we then converted the XYZ data to sRGB and used the Matlab image processing toolbox to display it. We later used sensor spectral responses (from the lecture notes, Nikon, QImaging and Kodak) to convert to RGB. The dehazing was done using the L* channel in CIELAB space (although we also investigated using a linear luminance channel).



	==Dehazing Algorithm==		==Dehazing Algorithm==

imported>Psych2012: /* Introduction */

2012-03-21T03:25:09Z

Introduction

← Older revision		Revision as of 03:25, 21 March 2012
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	Haze is caused by the scattering of Rayleigh and Mie light by particles in the atmosphere, such as droplets of water or smoke. These particles, which are between the viewer and a distant object, scatter light from other areas toward the viewer (airlight), and prevent some of the light from the distant object from reaching the viewer (see schematic). Restoring an image to non-hazy conditions is desirable because we like clear days. Since the amount of Rayleigh scattering increases proportional to <math>1/\lambda^4</math>, where <math>\lambda </math> is the wavelength of light, for longer wavelengths there will be less scattering, and thus the image will appear less hazy at these wavelengths.		Haze is caused by the scattering of Rayleigh and Mie light by particles in the atmosphere, such as droplets of water or smoke. These particles, which are between the viewer and a distant object, scatter light from other areas toward the viewer (airlight), and prevent some of the light from the distant object from reaching the viewer (see schematic). Restoring an image to non-hazy conditions is desirable because we like clear days. Since the amount of Rayleigh scattering increases proportional to <math>1/\lambda^4</math>, where <math>\lambda </math> is the wavelength of light, for longer wavelengths there will be less scattering, and thus the image will appear less hazy at these wavelengths.
	Here, we investigate using NIR and red spectral data to add detail and thus dehaze images, using a modified version of the algorithm described in [1]. Our study differs from the investigation in [1] as we do additional spectral characterization, made possible by the hyperspectral data made available to us. This algorithm takes detail from the long wavelength channel and adds it to a visible luminance channel to dehaze the image. Our investigation includes data from 400-1000 nm (as this spectral range is technically accessible with a silicon detector), and we investigate the best design for a NIR filter for dehazing, and the possibility of using the red camera sensor from three different commercial cameras to dehaze all three color channels. We perform a viewer study to assess the effectiveness of our dehazing. We also explain our results using spectral data and comparing to an [http://speclib.jpl.nasa.gov/ online database][2] of reflectance spectra. We also dehaze RGB images taken from the dataset from [1] available [http://ivrg.epfl.ch/supplementary_material/cvpr11/index.html here] compare dehazing of these images with NIR data (also available in the dataset) and with the R pixel values, obtaining (to us) more attractive results with the R camera sensor.		Here, we investigate using NIR and red spectral data to add detail and thus dehaze images, using a modified version of the algorithm described in [1]. Our study differs from the investigation in [1] as we do additional spectral characterization, made possible by the hyperspectral data made available to us. This algorithm takes detail from the long wavelength channel and adds it to a visible luminance channel to dehaze the image. Our investigation includes data from 400-1000 nm (as this spectral range is technically accessible with a silicon detector), and we investigate the best design for a NIR filter for dehazing, and the possibility of using the red camera sensor from three different commercial cameras to dehaze all three color channels. We perform a viewer study to assess the effectiveness of our dehazing. We also explain our results using spectral data and comparing to an [http://speclib.jpl.nasa.gov/ online database][2] of reflectance spectra. We also dehaze RGB images taken from the dataset from [1] available [http://ivrg.epfl.ch/supplementary_material/cvpr11/index.html here] compare dehazing of these images with NIR data (also available in the dataset) and with the R pixel values, obtaining (to us) more attractive results with the R camera sensor.
	~~[[File:dhjs-panoQI.png]]~~

	=Methods=		=Methods=

imported>Psych2012: /* Introduction */

2012-03-21T03:24:03Z

Introduction

← Older revision		Revision as of 03:24, 21 March 2012
Line 3:		Line 3:
	Haze is caused by the scattering of Rayleigh and Mie light by particles in the atmosphere, such as droplets of water or smoke. These particles, which are between the viewer and a distant object, scatter light from other areas toward the viewer (airlight), and prevent some of the light from the distant object from reaching the viewer (see schematic). Restoring an image to non-hazy conditions is desirable because we like clear days. Since the amount of Rayleigh scattering increases proportional to <math>1/\lambda^4</math>, where <math>\lambda </math> is the wavelength of light, for longer wavelengths there will be less scattering, and thus the image will appear less hazy at these wavelengths.		Haze is caused by the scattering of Rayleigh and Mie light by particles in the atmosphere, such as droplets of water or smoke. These particles, which are between the viewer and a distant object, scatter light from other areas toward the viewer (airlight), and prevent some of the light from the distant object from reaching the viewer (see schematic). Restoring an image to non-hazy conditions is desirable because we like clear days. Since the amount of Rayleigh scattering increases proportional to <math>1/\lambda^4</math>, where <math>\lambda </math> is the wavelength of light, for longer wavelengths there will be less scattering, and thus the image will appear less hazy at these wavelengths.
	Here, we investigate using NIR and red spectral data to add detail and thus dehaze images, using a modified version of the algorithm described in [1]. Our study differs from the investigation in [1] as we do additional spectral characterization, made possible by the hyperspectral data made available to us. This algorithm takes detail from the long wavelength channel and adds it to a visible luminance channel to dehaze the image. Our investigation includes data from 400-1000 nm (as this spectral range is technically accessible with a silicon detector), and we investigate the best design for a NIR filter for dehazing, and the possibility of using the red camera sensor from three different commercial cameras to dehaze all three color channels. We perform a viewer study to assess the effectiveness of our dehazing. We also explain our results using spectral data and comparing to an [http://speclib.jpl.nasa.gov/ online database][2] of reflectance spectra. We also dehaze RGB images taken from the dataset from [1] available [http://ivrg.epfl.ch/supplementary_material/cvpr11/index.html here] compare dehazing of these images with NIR data (also available in the dataset) and with the R pixel values, obtaining (to us) more attractive results with the R camera sensor.		Here, we investigate using NIR and red spectral data to add detail and thus dehaze images, using a modified version of the algorithm described in [1]. Our study differs from the investigation in [1] as we do additional spectral characterization, made possible by the hyperspectral data made available to us. This algorithm takes detail from the long wavelength channel and adds it to a visible luminance channel to dehaze the image. Our investigation includes data from 400-1000 nm (as this spectral range is technically accessible with a silicon detector), and we investigate the best design for a NIR filter for dehazing, and the possibility of using the red camera sensor from three different commercial cameras to dehaze all three color channels. We perform a viewer study to assess the effectiveness of our dehazing. We also explain our results using spectral data and comparing to an [http://speclib.jpl.nasa.gov/ online database][2] of reflectance spectra. We also dehaze RGB images taken from the dataset from [1] available [http://ivrg.epfl.ch/supplementary_material/cvpr11/index.html here] compare dehazing of these images with NIR data (also available in the dataset) and with the R pixel values, obtaining (to us) more attractive results with the R camera sensor.
	[[File:dhjs-panoQI.png~~\|1000px~~]]		[[File:dhjs-panoQI.png]]

	=Methods=		=Methods=

imported>Psych2012: /* Introduction */

2012-03-21T03:23:52Z

Introduction

← Older revision		Revision as of 03:23, 21 March 2012
Line 3:		Line 3:
	Haze is caused by the scattering of Rayleigh and Mie light by particles in the atmosphere, such as droplets of water or smoke. These particles, which are between the viewer and a distant object, scatter light from other areas toward the viewer (airlight), and prevent some of the light from the distant object from reaching the viewer (see schematic). Restoring an image to non-hazy conditions is desirable because we like clear days. Since the amount of Rayleigh scattering increases proportional to <math>1/\lambda^4</math>, where <math>\lambda </math> is the wavelength of light, for longer wavelengths there will be less scattering, and thus the image will appear less hazy at these wavelengths.		Haze is caused by the scattering of Rayleigh and Mie light by particles in the atmosphere, such as droplets of water or smoke. These particles, which are between the viewer and a distant object, scatter light from other areas toward the viewer (airlight), and prevent some of the light from the distant object from reaching the viewer (see schematic). Restoring an image to non-hazy conditions is desirable because we like clear days. Since the amount of Rayleigh scattering increases proportional to <math>1/\lambda^4</math>, where <math>\lambda </math> is the wavelength of light, for longer wavelengths there will be less scattering, and thus the image will appear less hazy at these wavelengths.
	Here, we investigate using NIR and red spectral data to add detail and thus dehaze images, using a modified version of the algorithm described in [1]. Our study differs from the investigation in [1] as we do additional spectral characterization, made possible by the hyperspectral data made available to us. This algorithm takes detail from the long wavelength channel and adds it to a visible luminance channel to dehaze the image. Our investigation includes data from 400-1000 nm (as this spectral range is technically accessible with a silicon detector), and we investigate the best design for a NIR filter for dehazing, and the possibility of using the red camera sensor from three different commercial cameras to dehaze all three color channels. We perform a viewer study to assess the effectiveness of our dehazing. We also explain our results using spectral data and comparing to an [http://speclib.jpl.nasa.gov/ online database][2] of reflectance spectra. We also dehaze RGB images taken from the dataset from [1] available [http://ivrg.epfl.ch/supplementary_material/cvpr11/index.html here] compare dehazing of these images with NIR data (also available in the dataset) and with the R pixel values, obtaining (to us) more attractive results with the R camera sensor.		Here, we investigate using NIR and red spectral data to add detail and thus dehaze images, using a modified version of the algorithm described in [1]. Our study differs from the investigation in [1] as we do additional spectral characterization, made possible by the hyperspectral data made available to us. This algorithm takes detail from the long wavelength channel and adds it to a visible luminance channel to dehaze the image. Our investigation includes data from 400-1000 nm (as this spectral range is technically accessible with a silicon detector), and we investigate the best design for a NIR filter for dehazing, and the possibility of using the red camera sensor from three different commercial cameras to dehaze all three color channels. We perform a viewer study to assess the effectiveness of our dehazing. We also explain our results using spectral data and comparing to an [http://speclib.jpl.nasa.gov/ online database][2] of reflectance spectra. We also dehaze RGB images taken from the dataset from [1] available [http://ivrg.epfl.ch/supplementary_material/cvpr11/index.html here] compare dehazing of these images with NIR data (also available in the dataset) and with the R pixel values, obtaining (to us) more attractive results with the R camera sensor.
			[[File:dhjs-panoQI.png\|1000px]]

	=Methods=		=Methods=

imported>Psych2012: /* Methods */

2012-03-21T03:19:02Z

Methods

← Older revision		Revision as of 03:19, 21 March 2012
Line 5:		Line 5:

	=Methods=		=Methods=
	We started with the hyperspectral panorama taken from the dish. Initially, we converted (scenes from) this data to the CIEXYZ space by interpolating the XYZ curves from ISET at the wavelengths at which the hyperspectral data was taken, and using these curves to weight the spectral data (see attached code). To view the image, we then converted the XYZ data to sRGB and used the Matlab image processing toolbox to display it. We later used sensor spectral responses (from the lecture notes, Nikon, QImaging and Kodak) to convert to RGB. The dehazing was done using the L* channel in CIELAB space (although we also investigated using a linear luminance channel).		We started with the hyperspectral panorama taken from the dish. Initially, we converted (scenes from) this data to the CIEXYZ space by interpolating the XYZ curves from ISET at the wavelengths at which the hyperspectral data was taken, and using these curves to weight the spectral data (see attached code). To view the image, we then converted the XYZ data to sRGB and used the Matlab image processing toolbox to display it. We later used sensor spectral responses (from the lecture notes, Nikon, QImaging and Kodak) to convert to RGB. The dehazing was done using the L* channel in CIELAB space (although we also investigated using a linear luminance channel). The panorama which we received in hyperspectral data is shown converted to sRGB below.



	==Dehazing Algorithm==		==Dehazing Algorithm==

imported>Psych2012: /* Future Work */

2012-03-21T03:16:57Z

Future Work

← Older revision		Revision as of 03:16, 21 March 2012
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	=Future Work=		=Future Work=
	#Since the best dehazing (in our opinion) was by the long wavelengths right at the end of the visible range (and right before the jump in reflectivity of vegetation), having a sensor with a sharp spectral function would help improve the dehazing. If this could be done effectively using a linear combination of RGB data to extrapolate intensities in a narrow-band at an optimal wavelength, the dehazing by this method could be improved. We touched on this when we used X-(0.194)Z to dehaze the dataset images.		#Since the best dehazing (in our opinion) was by the long wavelengths right at the end of the visible range (and right before the jump in reflectivity of vegetation), having a sensor with a sharp spectral function would help improve the dehazing. If this could be done effectively using a linear combination of RGB data to extrapolate intensities in a narrow-band at an optimal wavelength, the dehazing by this method could be improved. We touched on this when we used X-(0.194)Z to dehaze the dataset images.
	#The work by He et al. on dehazing using a dark channel prior [6] seems very promising. They essentially use the different transmission through haze of the different color channels to estimate the transmission of the haze and remove it. It is possible that the NIR data would ~~be more helpful in the~~ model, although the problem of decreased contrast would likely again cause problems. The colors seem much better preserved by this algorithm seem ~~better preserved~~ than with the algorithm used by Schaul et al. [1] and us! It would certainly be interesting to investigate this further.		#The work by He et al. on dehazing using a dark channel prior [6] seems very promising. They essentially use the different transmission through haze of the different color channels to estimate the transmission of the haze and remove it. It is possible that the NIR data would improve their model, although the problem of decreased contrast would likely again cause problems. The colors seem much better preserved by this algorithm seem than with the algorithm used by Schaul et al. [1] and us! It would certainly be interesting to investigate this further.
	#Create a metric for dehazing quality, eg. comparing to same pictures taken on a clear day, or using polarization data and algorithm from Schechner et al [5] (this takes advantage of the fact that airlight is partially polarized).		#Create a metric for dehazing quality, eg. comparing to same pictures taken on a clear day, or using polarization data and algorithm from Schechner et al [5] (this takes advantage of the fact that airlight is partially polarized).

imported>Psych2012: /* Future Work */

2012-03-21T03:15:48Z

Future Work

← Older revision		Revision as of 03:15, 21 March 2012
Line 136:		Line 136:

	=Future Work=		=Future Work=
			#Since the best dehazing (in our opinion) was by the long wavelengths right at the end of the visible range (and right before the jump in reflectivity of vegetation), having a sensor with a sharp spectral function would help improve the dehazing. If this could be done effectively using a linear combination of RGB data to extrapolate intensities in a narrow-band at an optimal wavelength, the dehazing by this method could be improved. We touched on this when we used X-(0.194)Z to dehaze the dataset images.
	#The work by He et al. on dehazing using a dark channel prior [6] seems very promising. They essentially use the different transmission through haze of the different color channels to estimate the transmission of the haze and remove it. It is possible that the NIR data would be more helpful in the model, although the problem of decreased contrast would likely again cause problems. The colors seem much better preserved by this algorithm seem better preserved than with the algorithm used by Schaul et al. [1] and us! It would certainly be interesting to investigate this further.		#The work by He et al. on dehazing using a dark channel prior [6] seems very promising. They essentially use the different transmission through haze of the different color channels to estimate the transmission of the haze and remove it. It is possible that the NIR data would be more helpful in the model, although the problem of decreased contrast would likely again cause problems. The colors seem much better preserved by this algorithm seem better preserved than with the algorithm used by Schaul et al. [1] and us! It would certainly be interesting to investigate this further.
	#Create a metric for dehazing quality, eg. comparing to same pictures taken on a clear day, or using polarization data and algorithm from Schechner et al [5] (this takes advantage of the fact that airlight is partially polarized).		#Create a metric for dehazing quality, eg. comparing to same pictures taken on a clear day, or using polarization data and algorithm from Schechner et al [5] (this takes advantage of the fact that airlight is partially polarized).
	*Since the best dehazing (in our opinion) was by the long wavelengths right at the end of the visible range (and right before the jump in reflectivity of vegetation), having a sensor with a sharp spectral function would help improve the dehazing. If this could be done effectively using a linear combination of RGB data to extrapolate intensities in a narrow-band at an optimal wavelength, the dehazing by this method could be improved. We touched on this when we used X-(0.78)Z to dehaze the dataset images.

	=References=		=References=