Pub Date : 2016-02-14DOI: 10.2352/ISSN.2470-1173.2016.6.RETINEX-018
A. Rizzi
{"title":"Designator Retinex, Milano Retinex and the locality issue","authors":"A. Rizzi","doi":"10.2352/ISSN.2470-1173.2016.6.RETINEX-018","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.6.RETINEX-018","url":null,"abstract":"","PeriodicalId":326060,"journal":{"name":"Retinex at 50","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116710854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-02-14DOI: 10.2352/ISSN.2470-1173.2016.6.RETINEX-019
M. McCourt, B. Blakeslee, D. Cope
The Oriented Difference of Gaussians (ODOG) model (6) was developed to gauge the degree to which “early” visual processes such as spatial filtering and response normalization could account for human brightness percepts in a set of canonical stimuli including the White effect (44-46), classical simultaneous brightness contrast (SBC) (25), and grating induction (GI) (3, 5, 20, 34, 37, 48). The ODOG model successfully predicts changes in the magnitude of the White effect (9) and GI (11) as a function of inducing grating spatial frequency and test patch height, as well as the relative magnitude of brightness variations in many other stimuli including the Hermann Grid (8), the Gelb Staircase (16,17), the Wertheimer-Benary Cross (4, 7, 8), Howe's variations on White’s stimulus (15, 28), Todorovic’s (43) and Williams, McCoy, & Purves’ (47) variations on the SBC stimulus (6, 12), the checkerboard induction stimulus (9, 19), the shifted White stimulus (9, 45), Adelson’s Checker-Shadow (1, 12), Snake stimulus (2, 8, 12, 41), and Corrugated Mondrian stimuli (1, 8), including Todorovic’s variation (7, 8, 43), Hillis & Brainard’s Paint/Shadow stimulus (12, 27), “remote” induction stimuli (8, 10, 32, 40), and in the probe discs placed in Cartier-Bresson photographs (12, 22).
{"title":"The Oriented Difference-of-Gaussians Model of Brightness Perception","authors":"M. McCourt, B. Blakeslee, D. Cope","doi":"10.2352/ISSN.2470-1173.2016.6.RETINEX-019","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.6.RETINEX-019","url":null,"abstract":"The Oriented Difference of Gaussians (ODOG) model (6) was developed to gauge the degree to which “early” visual processes such as spatial filtering and response normalization could account for human brightness percepts in a set of canonical stimuli including the White effect (44-46), classical simultaneous brightness contrast (SBC) (25), and grating induction (GI) (3, 5, 20, 34, 37, 48). The ODOG model successfully predicts changes in the magnitude of the White effect (9) and GI (11) as a function of inducing grating spatial frequency and test patch height, as well as the relative magnitude of brightness variations in many other stimuli including the Hermann Grid (8), the Gelb Staircase (16,17), the Wertheimer-Benary Cross (4, 7, 8), Howe's variations on White’s stimulus (15, 28), Todorovic’s (43) and Williams, McCoy, & Purves’ (47) variations on the SBC stimulus (6, 12), the checkerboard induction stimulus (9, 19), the shifted White stimulus (9, 45), Adelson’s Checker-Shadow (1, 12), Snake stimulus (2, 8, 12, 41), and Corrugated Mondrian stimuli (1, 8), including Todorovic’s variation (7, 8, 43), Hillis & Brainard’s Paint/Shadow stimulus (12, 27), “remote” induction stimuli (8, 10, 32, 40), and in the probe discs placed in Cartier-Bresson photographs (12, 22).","PeriodicalId":326060,"journal":{"name":"Retinex at 50","volume":"496 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115886099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-02-14DOI: 10.2352/ISSN.2470-1173.2016.6.RETINEX-318
J. Lisani, A. Petro, E. Provenzi, Catalina Sbert
We present a generalized white-patch technique able to rapidly detect color cast of natural images. Instead of relying on the chromatic information of a single perfectly reflective patch in the image, as pure white-patch models do, we consider a connected region of pixels that will serve as white reference for the method. The pixels belonging to the white reference region must comply with three properties: 1) they do not have to be completely saturated; 2) they must belong to the p% of pixels with brightest intensity in the whole image (where p is a parameter of the model); 3) the area of the connected region formed by these pix-els must overcome a threshold of significance A (a second parameter). Color cast is detected if the average intensity in the three separated chromatic channels RGB is distant enough from a neutral grey level, where the distance is measured through an angular metric.
{"title":"A generalized white-patch model for fast color cast detection in natural images","authors":"J. Lisani, A. Petro, E. Provenzi, Catalina Sbert","doi":"10.2352/ISSN.2470-1173.2016.6.RETINEX-318","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.6.RETINEX-318","url":null,"abstract":"We present a generalized white-patch technique able to rapidly detect color cast of natural images. Instead of relying on the chromatic information of a single perfectly reflective patch in the image, as pure white-patch models do, we consider a connected region of pixels that will serve as white reference for the method. The pixels belonging to the white reference region must comply with three properties: 1) they do not have to be completely saturated; 2) they must belong to the p% of pixels with brightest intensity in the whole image (where p is a parameter of the model); 3) the area of the connected region formed by these pix-els must overcome a threshold of significance A (a second parameter). Color cast is detected if the average intensity in the three separated chromatic channels RGB is distant enough from a neutral grey level, where the distance is measured through an angular metric.","PeriodicalId":326060,"journal":{"name":"Retinex at 50","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121411155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-02-14DOI: 10.2352/ISSN.2470-1173.2016.6.RETINEX-017
J. McCann
There are many different Retinex algorithms. They make different assumptions, and attempt to solve different problems. They have different goals, ground truths and output results. This “Retinex at 50 Workshop” session compares the variety of Retinex algorithms, along with their goals, ground truths that measure the success of their results. All Retinex algorithms use spatial comparisons to calculate the appearances of the entire scene. All Retinex algorithms need observer data to quantify human vision, so as to evaluate their accuracy. The most critical component of all Retinex experiments is the observer matches used to characterize human spatial vision. This paper reviews the experiments that have evolved as a result of Retinex Theory. They provide a very challenging data set for algorithms that predict appearance. Introduction Edwin Land coined the word Retinex in 1963. He used it to describe the theoretical need for three independent color channels to explain human color constancy.[1] The word was a contraction of “retina” and “cortex”. A “Retinex” is a theoretical color channel that makes spatial comparisons so as to calculate lightness sensations, namely the range of appearances between light and dark. Land had enthusiastically experimented with two-color projections in the late 1950’s and early 60’s.[2] By that time, he had hundreds of patents on many different photographic systems. He was well aware of the possibilities, and limitations, of silver halide photography. Before his Red and White light projection experiments, he accepted the standard explanation of color. Namely, color was the result of the local quanta catches of receptors with different spectral sensitivities. Human color vision was thought to behave the way that color film did; in that color was a local phenomenon that resulted from spectral responses within each very small image segment. The quanta catches of the triplet of retinal cones in a small retinal region generated color appearances. An accidental observation made a colleague in a late-night experiment changed all. The colleague remarked that there was more color than expected from mixtures of photographic separations using red and white lights. Land responded: “ Oh yes, that is adaptation.” At two o’clock in the morning, Land sat up in bed, and said : “Adaptation, what adaptation?” He immediately returned to the lab to repeat the experiment. For the rest of his life, human color vision was a favorite research area. What was it that Land had seen, so briefly, that made him return to the lab in the middle of the night? Human Trichromatic Color Theory and film have always been linked. When Thomas Young made his famous suggestion of human trichromacy in 1802, his colleague at the Royal Institution, Humphrey Davy, was studying a black and white photographic system. Young was the editor of the Institutions journal that described the work.[3] Young was well aware of silver halide’s response to light. That night, Land rea
{"title":"Retinex Algorithms: Many spatial processes used to solve many different problems","authors":"J. McCann","doi":"10.2352/ISSN.2470-1173.2016.6.RETINEX-017","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.6.RETINEX-017","url":null,"abstract":"There are many different Retinex algorithms. They make different assumptions, and attempt to solve different problems. They have different goals, ground truths and output results. This “Retinex at 50 Workshop” session compares the variety of Retinex algorithms, along with their goals, ground truths that measure the success of their results. All Retinex algorithms use spatial comparisons to calculate the appearances of the entire scene. All Retinex algorithms need observer data to quantify human vision, so as to evaluate their accuracy. The most critical component of all Retinex experiments is the observer matches used to characterize human spatial vision. This paper reviews the experiments that have evolved as a result of Retinex Theory. They provide a very challenging data set for algorithms that predict appearance. Introduction Edwin Land coined the word Retinex in 1963. He used it to describe the theoretical need for three independent color channels to explain human color constancy.[1] The word was a contraction of “retina” and “cortex”. A “Retinex” is a theoretical color channel that makes spatial comparisons so as to calculate lightness sensations, namely the range of appearances between light and dark. Land had enthusiastically experimented with two-color projections in the late 1950’s and early 60’s.[2] By that time, he had hundreds of patents on many different photographic systems. He was well aware of the possibilities, and limitations, of silver halide photography. Before his Red and White light projection experiments, he accepted the standard explanation of color. Namely, color was the result of the local quanta catches of receptors with different spectral sensitivities. Human color vision was thought to behave the way that color film did; in that color was a local phenomenon that resulted from spectral responses within each very small image segment. The quanta catches of the triplet of retinal cones in a small retinal region generated color appearances. An accidental observation made a colleague in a late-night experiment changed all. The colleague remarked that there was more color than expected from mixtures of photographic separations using red and white lights. Land responded: “ Oh yes, that is adaptation.” At two o’clock in the morning, Land sat up in bed, and said : “Adaptation, what adaptation?” He immediately returned to the lab to repeat the experiment. For the rest of his life, human color vision was a favorite research area. What was it that Land had seen, so briefly, that made him return to the lab in the middle of the night? Human Trichromatic Color Theory and film have always been linked. When Thomas Young made his famous suggestion of human trichromacy in 1802, his colleague at the Royal Institution, Humphrey Davy, was studying a black and white photographic system. Young was the editor of the Institutions journal that described the work.[3] Young was well aware of silver halide’s response to light. That night, Land rea","PeriodicalId":326060,"journal":{"name":"Retinex at 50","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129307210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-02-14DOI: 10.2352/ISSN.2470-1173.2016.6.RETINEX-317
Javier Vazquez-Corral, Syed Waqas Zamir, A. Galdran, David Pardo, Marcelo, Bertalmío
Comunicacio presentada al IS&T International Symposium on Electronic Imaging, celebrat del 14 al 18 de febrer de 2016 a San Francisco (CA, USA) i organitzat per la Society for Imaging Science and Technology.
Comunicacio presentada al IS&T International Symposium on Electronic Imaging, celebrat del 14 al 18 de febrer de 2016 a San Francisco (CA, USA) i organitzat per la Society for Imaging Science and Technology.
{"title":"Image processing applications through a variational perceptually-based color correction related to Retinex","authors":"Javier Vazquez-Corral, Syed Waqas Zamir, A. Galdran, David Pardo, Marcelo, Bertalmío","doi":"10.2352/ISSN.2470-1173.2016.6.RETINEX-317","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.6.RETINEX-317","url":null,"abstract":"Comunicacio presentada al IS&T International Symposium on Electronic Imaging, celebrat del 14 al 18 de febrer de 2016 a San Francisco (CA, USA) i organitzat per la Society for Imaging Science and Technology.","PeriodicalId":326060,"journal":{"name":"Retinex at 50","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127339174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-02-14DOI: 10.2352/ISSN.2470-1173.2016.6.RETINEX-020
Vassilios Vonikakis, Stefan Winkler
This paper presents a computational framework inspired by the center-surround antagonistic receptive fields of the human visual system. It demonstrates that, starting from the actual pixel value (center) and a low-pass estimation of the pixel’s neighborhood (surround) and using a mapping function inspired by the shunting inhibition mechanism, some widely used spatial image processing techniques can be implemented, including adaptive tone-mapping, local contrast enhancement, text binarization and local feature detection. As a result, it highlights the relations of these seemingly different applications with the early stages of the human visual system and draws insights about their characteristics. Introduction Center-surround antagonistic Receptive Fields (RFs) are abundant in the Human Visual System (HVS). They have been found in many areas, such as the retina, the Lateral Geniculate Nucleus, V1 or in higher visual areas. It seems that this is a typical strategy that the HVS employs for local signal comparisons, not only in vision but in other sensory areas as well. The RFs of center-surround cells comprise two separate concentric regions sampling the photoreceptor mosaic (namely the center and the surround) that act antagonistically on the final output of the cell. ON center-surround cells exhibit increased output with higher photoreceptor activity on their center and decreased output with increased activity on their surround. Conversely, for OFF center-surround cells, higher photoreceptor activity on the center has a negative impact on their output, whereas, increased photoreceptor activity on the surround increases their output. The size of the two regions defines the spatial frequency of sampling: smaller RF sizes sample finer details from the photoreceptor mosaic, while larger sizes encode coarser scales of the same signal. Center-surround cells are essentially a biological implementation of spatial filtering. Spatial filtering is a very broad term, encompassing any kind of filtering operations that depend on the local content of the signal and are not globally constant. Almost all existing image processing and computational photography techniques include some kind of spatial image processing. Modern denoising, local contrast enhancement, scale decomposition, exposure fusion, HDR tone mapping are some of them. Most of these methods have some common grounds with the basic computational models of the early stages of the HVS. However these similarities are not always so evident. In this paper, we start from the computational model of the first stages of HVS, developed by Grossberg [24], and we adapt it for image processing operations. Explicitly modeling HVS is out of the scope of this paper. We rather draw inspiration from it in order to address real-world imaging problems. More specifically, we define a framework, inspired by Grossberg’s theory, that describes center-surround signal interactions. We show that such a framework can give rise to
{"title":"A center-surround framework for spatial image processing","authors":"Vassilios Vonikakis, Stefan Winkler","doi":"10.2352/ISSN.2470-1173.2016.6.RETINEX-020","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.6.RETINEX-020","url":null,"abstract":"This paper presents a computational framework inspired by the center-surround antagonistic receptive fields of the human visual system. It demonstrates that, starting from the actual pixel value (center) and a low-pass estimation of the pixel’s neighborhood (surround) and using a mapping function inspired by the shunting inhibition mechanism, some widely used spatial image processing techniques can be implemented, including adaptive tone-mapping, local contrast enhancement, text binarization and local feature detection. As a result, it highlights the relations of these seemingly different applications with the early stages of the human visual system and draws insights about their characteristics. Introduction Center-surround antagonistic Receptive Fields (RFs) are abundant in the Human Visual System (HVS). They have been found in many areas, such as the retina, the Lateral Geniculate Nucleus, V1 or in higher visual areas. It seems that this is a typical strategy that the HVS employs for local signal comparisons, not only in vision but in other sensory areas as well. The RFs of center-surround cells comprise two separate concentric regions sampling the photoreceptor mosaic (namely the center and the surround) that act antagonistically on the final output of the cell. ON center-surround cells exhibit increased output with higher photoreceptor activity on their center and decreased output with increased activity on their surround. Conversely, for OFF center-surround cells, higher photoreceptor activity on the center has a negative impact on their output, whereas, increased photoreceptor activity on the surround increases their output. The size of the two regions defines the spatial frequency of sampling: smaller RF sizes sample finer details from the photoreceptor mosaic, while larger sizes encode coarser scales of the same signal. Center-surround cells are essentially a biological implementation of spatial filtering. Spatial filtering is a very broad term, encompassing any kind of filtering operations that depend on the local content of the signal and are not globally constant. Almost all existing image processing and computational photography techniques include some kind of spatial image processing. Modern denoising, local contrast enhancement, scale decomposition, exposure fusion, HDR tone mapping are some of them. Most of these methods have some common grounds with the basic computational models of the early stages of the HVS. However these similarities are not always so evident. In this paper, we start from the computational model of the first stages of HVS, developed by Grossberg [24], and we adapt it for image processing operations. Explicitly modeling HVS is out of the scope of this paper. We rather draw inspiration from it in order to address real-world imaging problems. More specifically, we define a framework, inspired by Grossberg’s theory, that describes center-surround signal interactions. We show that such a framework can give rise to","PeriodicalId":326060,"journal":{"name":"Retinex at 50","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124680652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-02-14DOI: 10.2352/ISSN.2470-1173.2016.6.RETINEX-021
M. Rudd
{"title":"Retinex-like computations in human lightness perception and their possible realization in visual cortex","authors":"M. Rudd","doi":"10.2352/ISSN.2470-1173.2016.6.RETINEX-021","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.6.RETINEX-021","url":null,"abstract":"","PeriodicalId":326060,"journal":{"name":"Retinex at 50","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130907623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-02-14DOI: 10.2352/ISSN.2470-1173.2016.6.RETINEX-316
M. Bertalmío
Comunicacio presentada al IS&T International Symposium on Electronic Imaging, celebrat del 14 al 18 de febrer de 2016 a San Francisco (CA, USA) i organitzat per la Society for Imaging Science and Technology.
Comunicacio presentada al IS&T International Symposium on Electronic Imaging, celebrat del 14 al 18 de febrer de 2016 a San Francisco (CA, USA) i organitzat per la Society for Imaging Science and Technology.
{"title":"Connections between Retinex, neural models and variational methods","authors":"M. Bertalmío","doi":"10.2352/ISSN.2470-1173.2016.6.RETINEX-316","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.6.RETINEX-316","url":null,"abstract":"Comunicacio presentada al IS&T International Symposium on Electronic Imaging, celebrat del 14 al 18 de febrer de 2016 a San Francisco (CA, USA) i organitzat per la Society for Imaging Science and Technology.","PeriodicalId":326060,"journal":{"name":"Retinex at 50","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131880699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-02-14DOI: 10.2352/ISSN.2470-1173.2016.6.RETINEX-319
G. Gianini
{"title":"Statistical Aspects of Space Sampling in Retinex models","authors":"G. Gianini","doi":"10.2352/ISSN.2470-1173.2016.6.RETINEX-319","DOIUrl":"https://doi.org/10.2352/ISSN.2470-1173.2016.6.RETINEX-319","url":null,"abstract":"","PeriodicalId":326060,"journal":{"name":"Retinex at 50","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124059995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-02-14DOI: 10.2352/ISSN.2470-1173.2016.6.RETINEX-023
D. Marini, C. Bonanomi, A. Rizzi
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 fr
{"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":"https://doi.org/10.2352/ISSN.2470-1173.2016.6.RETINEX-023","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 fr","PeriodicalId":326060,"journal":{"name":"Retinex at 50","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129084391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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