{"title":"Processing astro-photographs using Retinex based methods","authors":"D. Marini, C. Bonanomi, A. Rizzi","doi":"10.2352/ISSN.2470-1173.2016.6.RETINEX-023","DOIUrl":null,"url":null,"abstract":"Stars, galaxies and some nebulae emit light, differently from solar system planets and satellites that mainly reflect Sun’s light or nebulae that also partly reflect nearby stars light. Light emission is concentrated on specific wavelengths corresponding to transition states of atoms that compose the object. Professional photographers and astronomers use special narrow band filters to detect spectral light emission. Using monochromatic CCD cameras a multi filter photograph can be taken, producing at least long, middle and short wavelength snapshots that can be processed to give full color pictures. Amateurs can use wide-band filters or even color cameras. Colors in astrophotography do not correspond to perceivable colors by human vision system (HVS) and our visual system did not evolve to perceive these kinds of images. Any way we still have to consider our perception when creating pictures to observe cosmic objects photos, that have been rendered using the so called representative colors, selected to show the captured wavelength bands with the purpose to make visible what is scientifically relevant. The typical application field of the Retinex based algorithms is that of natural images, since their purpose is to simulate some behaviors of the human visual system. However we can use HVS properties to enhance astrophotographs and increase local contrast, thus allowing researchers to detect non-visible structures and lay people to be fascinated by richness of cosmic objects. We will present the results of applications of some Retinex based algorithms to astrophotographs. We will discuss their efficacy, compared to traditional methods and discuss possible developments. Introduction Since the launch of Hubble telescope (1990), the first out of atmosphere orbiting telescope, a large amount of new photographs of deep sky objects have been acquired for scientific research purpose and public distribution. A famous picture that contributed to diffuse discoveries of the structure of the universe is the socalled Pillars of Creation in the Eagle nebula Messier 16 (see figure 1 – downloaded from [1]). Let’s explain this image. The staircase structure is due to the structure of Hubble telescope camera system, WFPC-2 (Wide Field/Planet Camera-2 wiffpick) [2], composed of 4 cameras, the top right one having double resolution to observe details of planets, to be down scaled to compose the large field picture. The second observation is the peculiar color channels distribution. The image has been captured through three narrow band filters centered around the emission lines of specific atoms of gas molecules of nebulae: O III (Oxygen III, 501.2 nm Δλ 2.7 nm), Hα (Hydrogen alpha, 656.4 nm Δλ 3.5 nm) and SII (Sulfur II, 673.2 nm Δλ 4.72nm). If we interpret these wavelengths as color bands we see that there is no blue component: OIII is around green and SII, Hα are in the orange-red interval. To explore this color rendering we have downloaded the three band pictures from Hubble Telescope repository [3] and processed for color display creating what in astronomer jargon is a “representative color”, not to be confused with the arbitrary “false color” [4]. The three sub-images in figure 2 have been obtained by assigning channels as Sulfur to red, Hydrogen to green and Oxygen to blue (S=R, H=G, O=B). The second sub-image has been obtained exchanging Hydrogen with Sulfur (H=R, S=G, O=B) and the third sub-image is the assignment O=R, S=G and H=B. Color rendering vary largely among the three distributions and with respect to figure1. We note that the left sub-image of figure 2 (where the color distribution among channels is the common one) is quite different from the one published in hubblesite.org (figure 1). First of all, the mosaic of the four tiles must be aligned, secondly, the colors have been corrected and third the contrast improved. Color correction and contrast improvement are necessary to increase the display of information for the scientist, rather than to obtain beautiful or natural images. They are necessary not to make the pictures more beauty, rather to empower the efficacy of information display for scientist to better understand the nature and structure of the cosmic object and get all the subtleties of the gas distribution. Normally astronomers use GIMP, Photoshop or similar programs for these operations and sometime they use the “false colors” in order to show structures that would be otherwise invisible, thus exploiting different contrast adjustments at different wavelengths. The issues we are facing here, concern the display of complex information, rather than the perception of natural scene (even if astronomical). Our aim is therefore to apply concept of color vision to these kinds of images and to verify if the adoption of algorithms derived from the Retinex theory can provide better results, with less lengthy trial and error work. The same problem applies also to astro-photographs taken with amateur telescopes using color camera rather than monochromatic camera with filters. Retinex algorithms are spatial color algorithms (SCA) [5] inspired by the HVS behavior. They attempt to extract the maximum visual information from images, whose acquisition process and context are unknown. Research has shown that SC algorithms generate the final appearance of an image starting from very poor images in terms of dynamic range, contrast and chromatic content. Mimicking HVS, whose retina has a large overlap between long and medium wavelengths (causing a low L/M ratio), SC algorithms are able to perform an unsupervised enhancement that recovers important visual information from poor input, processing independently the three channels. In some astronomical photographs information about the incoming radiance could be available or approximated from known characteristics of the emitting source (radiance, chromaticity) and the imaging system, but in most cases photographs are not calibrated in the sense that to each digit does not correspond a known invertible function of the radiance. This could be achievable with radiometric measurements, but radiometry is not used for viewing astronomic images. When cosmic object visual rendering is required the purpose of the astronomer is to display some relevant information making it clearly distinguishable. In this case HVS/SCA contrast enhancement has been proved to be effective [6]. Moreover we note that processing color channels independently increases the possibility to maximize the visual information in each channel without adding any cross correlation during processing. The idea of applying HVS inspired method to process astrophotographs is not new, but at our knowledge very few experiences have been presented. The Retinex method proposed in [7] has been applied to natural photographs, a single example of a photo of the Moon is presented in this site: http://dragon.larc.nasa.gov/retinex/Lunar_Orbiter/. Based on the same algorithms as in [7], F. Weinhaus has implemented a Retinex tool into ImageMagick processing function library [8]. Similar tools like e.g. STRESS [9] are also included in the GIMP program and sometimes used for processing astrophotographs. The use of Retinex is also discussed in forums about the image-processing program StarTools by Ivo Jager, who claims to be using Retinex: “The Dynamic range optimization in StarTools is based on a Retinex & Local Histogram Stretching hybrid algorithm” [10]. Gupta and Mandal in [11] consider Retinex as a processing technique to enhance histograms in astrophotographs but we could not find any effective example. A method to estimate fractional gradient to stretch the histogram has been proposed by Sparavigna et al. [12] and tested on some astronomic photographs. To conclude this short review we note that no specific research on HVS inspired methods for processing astro-photographs seems to have been published. In the section Instruments and Methods we will briefly describe the selected algorithms, details are left to the literature, we also identify three kinds of astronomical photographs that show different features relevant for the SCA processing. Moreover we recall the main steps from the astro photo to the SCA processing. In section Experiment Results we present and discuss the results obtained for the chosen astro-photographs and algorithms. In Conclusions we will discuss the results and we will outline possible future developments for making practical SCA processing for astro imaging. Instruments and Methods First of all it is worth to note the characteristics of astronomical images. In natural images we can have extended areas of constant or slowly varying color and contrast, we can have a relationship between a background and a foreground with out of focus regions. Edges in natural images correspond to different objects with some regular on at least known shape. In astro photography we have a huge amount of point sources (the stars). Stars can appear as standalone high luminous points or dense or sparse cluster; they can be aggregated around galaxies or be surrounded by gas nebulae. Galaxies have an irregular light distribution, while nebulae can show more slow varying contrast and luminosity in the different spectral bands. There is no foreground-background relationship and the image field is at the same focus. Most galaxy and nebulae images frequently do not have clear edges. These characteristics of astro images are important to understand how critical is the problem of noise, of contrast and of color rendering to make visible important information. In other words: to prepare the image for the further task of recognition and interpretation. The first step in our work has been therefore to select representative images of the different kinds of deep sky objects and, second, to compare the efficacy of the chosen Retinex algorithms. Image Selection The images selection has been driven by the necessity to consider different kinds of sky ","PeriodicalId":326060,"journal":{"name":"Retinex at 50","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Retinex at 50","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2352/ISSN.2470-1173.2016.6.RETINEX-023","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
Stars, galaxies and some nebulae emit light, differently from solar system planets and satellites that mainly reflect Sun’s light or nebulae that also partly reflect nearby stars light. Light emission is concentrated on specific wavelengths corresponding to transition states of atoms that compose the object. Professional photographers and astronomers use special narrow band filters to detect spectral light emission. Using monochromatic CCD cameras a multi filter photograph can be taken, producing at least long, middle and short wavelength snapshots that can be processed to give full color pictures. Amateurs can use wide-band filters or even color cameras. Colors in astrophotography do not correspond to perceivable colors by human vision system (HVS) and our visual system did not evolve to perceive these kinds of images. Any way we still have to consider our perception when creating pictures to observe cosmic objects photos, that have been rendered using the so called representative colors, selected to show the captured wavelength bands with the purpose to make visible what is scientifically relevant. The typical application field of the Retinex based algorithms is that of natural images, since their purpose is to simulate some behaviors of the human visual system. However we can use HVS properties to enhance astrophotographs and increase local contrast, thus allowing researchers to detect non-visible structures and lay people to be fascinated by richness of cosmic objects. We will present the results of applications of some Retinex based algorithms to astrophotographs. We will discuss their efficacy, compared to traditional methods and discuss possible developments. Introduction Since the launch of Hubble telescope (1990), the first out of atmosphere orbiting telescope, a large amount of new photographs of deep sky objects have been acquired for scientific research purpose and public distribution. A famous picture that contributed to diffuse discoveries of the structure of the universe is the socalled Pillars of Creation in the Eagle nebula Messier 16 (see figure 1 – downloaded from [1]). Let’s explain this image. The staircase structure is due to the structure of Hubble telescope camera system, WFPC-2 (Wide Field/Planet Camera-2 wiffpick) [2], composed of 4 cameras, the top right one having double resolution to observe details of planets, to be down scaled to compose the large field picture. The second observation is the peculiar color channels distribution. The image has been captured through three narrow band filters centered around the emission lines of specific atoms of gas molecules of nebulae: O III (Oxygen III, 501.2 nm Δλ 2.7 nm), Hα (Hydrogen alpha, 656.4 nm Δλ 3.5 nm) and SII (Sulfur II, 673.2 nm Δλ 4.72nm). If we interpret these wavelengths as color bands we see that there is no blue component: OIII is around green and SII, Hα are in the orange-red interval. To explore this color rendering we have downloaded the three band pictures from Hubble Telescope repository [3] and processed for color display creating what in astronomer jargon is a “representative color”, not to be confused with the arbitrary “false color” [4]. The three sub-images in figure 2 have been obtained by assigning channels as Sulfur to red, Hydrogen to green and Oxygen to blue (S=R, H=G, O=B). The second sub-image has been obtained exchanging Hydrogen with Sulfur (H=R, S=G, O=B) and the third sub-image is the assignment O=R, S=G and H=B. Color rendering vary largely among the three distributions and with respect to figure1. We note that the left sub-image of figure 2 (where the color distribution among channels is the common one) is quite different from the one published in hubblesite.org (figure 1). First of all, the mosaic of the four tiles must be aligned, secondly, the colors have been corrected and third the contrast improved. Color correction and contrast improvement are necessary to increase the display of information for the scientist, rather than to obtain beautiful or natural images. They are necessary not to make the pictures more beauty, rather to empower the efficacy of information display for scientist to better understand the nature and structure of the cosmic object and get all the subtleties of the gas distribution. Normally astronomers use GIMP, Photoshop or similar programs for these operations and sometime they use the “false colors” in order to show structures that would be otherwise invisible, thus exploiting different contrast adjustments at different wavelengths. The issues we are facing here, concern the display of complex information, rather than the perception of natural scene (even if astronomical). Our aim is therefore to apply concept of color vision to these kinds of images and to verify if the adoption of algorithms derived from the Retinex theory can provide better results, with less lengthy trial and error work. The same problem applies also to astro-photographs taken with amateur telescopes using color camera rather than monochromatic camera with filters. Retinex algorithms are spatial color algorithms (SCA) [5] inspired by the HVS behavior. They attempt to extract the maximum visual information from images, whose acquisition process and context are unknown. Research has shown that SC algorithms generate the final appearance of an image starting from very poor images in terms of dynamic range, contrast and chromatic content. Mimicking HVS, whose retina has a large overlap between long and medium wavelengths (causing a low L/M ratio), SC algorithms are able to perform an unsupervised enhancement that recovers important visual information from poor input, processing independently the three channels. In some astronomical photographs information about the incoming radiance could be available or approximated from known characteristics of the emitting source (radiance, chromaticity) and the imaging system, but in most cases photographs are not calibrated in the sense that to each digit does not correspond a known invertible function of the radiance. This could be achievable with radiometric measurements, but radiometry is not used for viewing astronomic images. When cosmic object visual rendering is required the purpose of the astronomer is to display some relevant information making it clearly distinguishable. In this case HVS/SCA contrast enhancement has been proved to be effective [6]. Moreover we note that processing color channels independently increases the possibility to maximize the visual information in each channel without adding any cross correlation during processing. The idea of applying HVS inspired method to process astrophotographs is not new, but at our knowledge very few experiences have been presented. The Retinex method proposed in [7] has been applied to natural photographs, a single example of a photo of the Moon is presented in this site: http://dragon.larc.nasa.gov/retinex/Lunar_Orbiter/. Based on the same algorithms as in [7], F. Weinhaus has implemented a Retinex tool into ImageMagick processing function library [8]. Similar tools like e.g. STRESS [9] are also included in the GIMP program and sometimes used for processing astrophotographs. The use of Retinex is also discussed in forums about the image-processing program StarTools by Ivo Jager, who claims to be using Retinex: “The Dynamic range optimization in StarTools is based on a Retinex & Local Histogram Stretching hybrid algorithm” [10]. Gupta and Mandal in [11] consider Retinex as a processing technique to enhance histograms in astrophotographs but we could not find any effective example. A method to estimate fractional gradient to stretch the histogram has been proposed by Sparavigna et al. [12] and tested on some astronomic photographs. To conclude this short review we note that no specific research on HVS inspired methods for processing astro-photographs seems to have been published. In the section Instruments and Methods we will briefly describe the selected algorithms, details are left to the literature, we also identify three kinds of astronomical photographs that show different features relevant for the SCA processing. Moreover we recall the main steps from the astro photo to the SCA processing. In section Experiment Results we present and discuss the results obtained for the chosen astro-photographs and algorithms. In Conclusions we will discuss the results and we will outline possible future developments for making practical SCA processing for astro imaging. Instruments and Methods First of all it is worth to note the characteristics of astronomical images. In natural images we can have extended areas of constant or slowly varying color and contrast, we can have a relationship between a background and a foreground with out of focus regions. Edges in natural images correspond to different objects with some regular on at least known shape. In astro photography we have a huge amount of point sources (the stars). Stars can appear as standalone high luminous points or dense or sparse cluster; they can be aggregated around galaxies or be surrounded by gas nebulae. Galaxies have an irregular light distribution, while nebulae can show more slow varying contrast and luminosity in the different spectral bands. There is no foreground-background relationship and the image field is at the same focus. Most galaxy and nebulae images frequently do not have clear edges. These characteristics of astro images are important to understand how critical is the problem of noise, of contrast and of color rendering to make visible important information. In other words: to prepare the image for the further task of recognition and interpretation. The first step in our work has been therefore to select representative images of the different kinds of deep sky objects and, second, to compare the efficacy of the chosen Retinex algorithms. Image Selection The images selection has been driven by the necessity to consider different kinds of sky