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Camera Raw Workflows
RAW WORKFLOWS: FROM CAMERA TO POST Copyright 2007, Jason Rodriguez, Silicon Imaging, Inc. Introduction What is a RAW file format, and what cameras shoot to these formats? How does working with RAW file-format cameras change the way I shoot? What changes are happening inside the camera I need to be aware of, and what happens when I go into post? What are the available post paths? Is there just one, or are there many ways to reach my end goals? What post tools support RAW file format workflows? How do RAW codecs like CineForm RAW enable me to work faster and with more efficiency? What is a RAW file? In simplest terms is the native digital data off the sensor's A/D converter with no further destructive DSP processing applied Derived from a photometrically linear data source, or can be reconstructed to produce data that directly correspond to the light that was captured by the sensor at the time of exposure (i.e., LOG->Lin reverse LUT) Photometrically Linear 1:1 Photons Digital Values Doubling of light means doubling of digitally encoded value What is a RAW file? In film-analogy would be termed a “digital negative” because it is a latent representation of the light that was captured by the sensor (up to the limit of the full-well capacity of the sensor) “RAW” cameras include Thomson Viper, Arri D-20, Dalsa Evolution 4K, Silicon Imaging SI-2K, Red One, Vision Research Phantom, noXHD, Reel-Stream “Quasi-RAW” cameras include the Panavision Genesis In-Camera Processing Most non-RAW cameras on the market record to 8-bit YUV formats -
Measuring Camera Shannon Information Capacity with a Siemens Star Image
https://doi.org/10.2352/ISSN.2470-1173.2020.9.IQSP-347 © 2020, Society for Imaging Science and Technology Measuring camera Shannon Information Capacity with a Siemens Star Image Norman L. Koren, Imatest LLC, Boulder, Colorado, USA Abstract Measurement background Shannon information capacity, which can be expressed as bits per To measure signal and noise at the same location, we use an pixel or megabits per image, is an excellent figure of merit for pre- image of a sinusoidal Siemens-star test chart consisting of ncycles dicting camera performance for a variety of machine vision appli- total cycles, which we analyze by dividing the star into k radial cations, including medical and automotive imaging systems. Its segments (32 or 64), each of which is subdivided into m angular strength is that is combines the effects of sharpness (MTF) and segments (8, 16, or 24) of length Pseg. The number sine wave noise, but it has not been widely adopted because it has been cycles in each angular segment is 푛 = 푛푐푦푐푙푒푠/푚. difficult to measure and has never been standardized. We have developed a method for conveniently measuring inform- ation capacity from images of the familiar sinusoidal Siemens Star chart. The key is that noise is measured in the presence of the image signal, rather than in a separate location where image processing may be different—a commonplace occurrence with bilateral filters. The method also enables measurement of SNRI, which is a key performance metric for object detection. Information capacity is strongly affected by sensor noise, lens quality, ISO speed (Exposure Index), and the demosaicing algo- rithm, which affects aliasing. -
Multi-Frame Demosaicing and Super-Resolution of Color Images
1 Multi-Frame Demosaicing and Super-Resolution of Color Images Sina Farsiu∗, Michael Elad ‡ , Peyman Milanfar § Abstract In the last two decades, two related categories of problems have been studied independently in the image restoration literature: super-resolution and demosaicing. A closer look at these problems reveals the relation between them, and as conventional color digital cameras suffer from both low-spatial resolution and color-filtering, it is reasonable to address them in a unified context. In this paper, we propose a fast and robust hybrid method of super-resolution and demosaicing, based on a MAP estimation technique by minimizing a multi-term cost function. The L 1 norm is used for measuring the difference between the projected estimate of the high-resolution image and each low-resolution image, removing outliers in the data and errors due to possibly inaccurate motion estimation. Bilateral regularization is used for spatially regularizing the luminance component, resulting in sharp edges and forcing interpolation along the edges and not across them. Simultaneously, Tikhonov regularization is used to smooth the chrominance components. Finally, an additional regularization term is used to force similar edge location and orientation in different color channels. We show that the minimization of the total cost function is relatively easy and fast. Experimental results on synthetic and real data sets confirm the effectiveness of our method. ∗Corresponding author: Electrical Engineering Department, University of California Santa Cruz, Santa Cruz CA. 95064 USA. Email: [email protected], Phone:(831)-459-4141, Fax: (831)-459-4829 ‡ Computer Science Department, The Technion, Israel Institute of Technology, Israel. -
Demosaicking: Color Filter Array Interpolation [Exploring the Imaging
[Bahadir K. Gunturk, John Glotzbach, Yucel Altunbasak, Ronald W. Schafer, and Russel M. Mersereau] FLOWER PHOTO © PHOTO FLOWER MEDIA, 1991 21ST CENTURY PHOTO:CAMERA AND BACKGROUND ©VISION LTD. DIGITAL Demosaicking: Color Filter Array Interpolation [Exploring the imaging process and the correlations among three color planes in single-chip digital cameras] igital cameras have become popular, and many people are choosing to take their pic- tures with digital cameras instead of film cameras. When a digital image is recorded, the camera needs to perform a significant amount of processing to provide the user with a viewable image. This processing includes correction for sensor nonlinearities and nonuniformities, white balance adjustment, compression, and more. An important Dpart of this image processing chain is color filter array (CFA) interpolation or demosaicking. A color image requires at least three color samples at each pixel location. Computer images often use red (R), green (G), and blue (B). A camera would need three separate sensors to completely meas- ure the image. In a three-chip color camera, the light entering the camera is split and projected onto each spectral sensor. Each sensor requires its proper driving electronics, and the sensors have to be registered precisely. These additional requirements add a large expense to the system. Thus, many cameras use a single sensor covered with a CFA. The CFA allows only one color to be measured at each pixel. This means that the camera must estimate the missing two color values at each pixel. This estimation process is known as demosaicking. Several patterns exist for the filter array. -
An Overview of State-Of-The-Art Denoising and Demosaicking Techniques: Toward a Unified Framework for Handling Artifacts During Image Reconstruction
AN OVERVIEW OF STATE-OF-THE-ART DENOISING AND DEMOSAICKING TECHNIQUES: TOWARD A UNIFIED FRAMEWORK FOR HANDLING ARTIFACTS DURING IMAGE RECONSTRUCTION Bart Goossens∗, Hiep Luong, Jan Aelterman, Aleksandra Pižurica and Wilfried Philips Ghent University - TELIN - IPI - iMinds Sint-Pietersnieuwstraat 41, B-9000 Ghent, Belgium [email protected] ABSTRACT RAW Updated input image RAW image Pushing the physical limits of the camera sensors brings significant challenges to image post-processing techniques, Degradation model Impose prior knowledge which often have to work under various constraints, such as (blur, signal-dependent noise, demosaicking…) w.r.t. undegraded image (e.g., wavelets, shearlets) low-power, limited memory and computational resources. Other Iterative platforms, e.g., desktop PCs suffer less from these issues, solver Reconstructed allowing extra room for improved reconstruction. Moreover, the image captured images are subject to many sources of imperfection (e.g., noise, blur, saturation) which complicate the processing. In this paper, we give an overview of some recent work aimed at Fig. 1. A unified reconstruction framework. overcoming these problems. We focus especially on denoising, deblurring and demosaicking techniques. techniques have been developed that jointly solve several degradations (e.g., noise, demosaicking, blur, saturation) at I. INTRODUCTION once, using the mathematical framework of estimation theory, leading to much better reconstruction results. Several notable In the past decade, there has been a significant increase in examples are: joint denoising and demosaicking [1]–[4], joint diversity of both display devices and image acquisition devices, demosaicking and deconvolution [5]–[8], high dynamic range going from tiny cameras in mobile phones to 100 megapixel reconstruction and denoising [9], [10] and declipping and (MP) digital cameras. -
Comparison of Color Demosaicing Methods Olivier Losson, Ludovic Macaire, Yanqin Yang
Comparison of color demosaicing methods Olivier Losson, Ludovic Macaire, Yanqin Yang To cite this version: Olivier Losson, Ludovic Macaire, Yanqin Yang. Comparison of color demosaicing methods. Advances in Imaging and Electron Physics, Elsevier, 2010, 162, pp.173-265. 10.1016/S1076-5670(10)62005-8. hal-00683233 HAL Id: hal-00683233 https://hal.archives-ouvertes.fr/hal-00683233 Submitted on 28 Mar 2012 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Comparison of color demosaicing methods a, a a O. Losson ∗, L. Macaire , Y. Yang a Laboratoire LAGIS UMR CNRS 8146 – Bâtiment P2 Université Lille1 – Sciences et Technologies, 59655 Villeneuve d’Ascq Cedex, France Keywords: Demosaicing, Color image, Quality evaluation, Comparison criteria 1. Introduction Today, the majority of color cameras are equipped with a single CCD (Charge- Coupled Device) sensor. The surface of such a sensor is covered by a color filter array (CFA), which consists in a mosaic of spectrally selective filters, so that each CCD ele- ment samples only one of the three color components Red (R), Green (G) or Blue (B). The Bayer CFA is the most widely used one to provide the CFA image where each pixel is characterized by only one single color component. -
Learning Deep Convolutional Networks for Demosaicing Nai-Sheng Syu∗, Yu-Sheng Chen∗, Yung-Yu Chuang
1 Learning Deep Convolutional Networks for Demosaicing Nai-Sheng Syu∗, Yu-Sheng Chen∗, Yung-Yu Chuang Abstract—This paper presents a comprehensive study of ap- for reducing visual artifacts as much as possible. However, plying the convolutional neural network (CNN) to solving the most researches only focus on one of them. demosaicing problem. The paper presents two CNN models that The Bayer filter is the most popular CFA [5] and has been learn end-to-end mappings between the mosaic samples and the original image patches with full information. In the case the widely used in both academic researches and real camera Bayer color filter array (CFA) is used, an evaluation on popular manufacturing. It samples the green channel with a quincunx benchmarks confirms that the data-driven, automatically learned grid while sampling red and blue channels by a rectangular features by the CNN models are very effective and our best grid. The higher sampling rate for the green component is con- proposed CNN model outperforms the current state-of-the-art sidered consistent with the human visual system. Most demo- algorithms. Experiments show that the proposed CNN models can perform equally well in both the sRGB space and the saicing algorithms are designed specifically for the Bayer CFA. linear space. It is also demonstrated that the CNN model can They can be roughly divided into two groups, interpolation- perform joint denoising and demosaicing. The CNN model is based methods [6], [7], [8], [9], [10], [11], [12], [13], [14] very flexible and can be easily adopted for demosaicing with any and dictionary-based methods [15], [16]. -
Multi-Frame Demosaicing and Super-Resolution of Color Images
IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 15, NO. 1, JANUARY 2006 141 Multiframe Demosaicing and Super-Resolution of Color Images Sina Farsiu, Michael Elad, and Peyman Milanfar, Senior Member, IEEE Abstract—In the last two decades, two related categories of prob- lems have been studied independently in image restoration liter- ature: super-resolution and demosaicing. A closer look at these problems reveals the relation between them, and, as conventional color digital cameras suffer from both low-spatial resolution and color-filtering, it is reasonable to address them in a unified con- text. In this paper, we propose a fast and robust hybrid method of super-resolution and demosaicing, based on a maximum a pos- teriori estimation technique by minimizing a multiterm cost func- tion. The I norm is used for measuring the difference between the projected estimate of the high-resolution image and each low- resolution image, removing outliers in the data and errors due to possibly inaccurate motion estimation. Bilateral regularization is used for spatially regularizing the luminance component, resulting in sharp edges and forcing interpolation along the edges and not across them. Simultaneously, Tikhonov regularization is used to smooth the chrominance components. Finally, an additional reg- ularization term is used to force similar edge location and orien- tation in different color channels. We show that the minimization of the total cost function is relatively easy and fast. Experimental results on synthetic and real data sets confirm the effectiveness of our method. Index Terms—Color enhancement, demosaicing, image restora- tion, robust estimation, robust regularization, super-resolution. I. INTRODUCTION EVERAL distorting processes affect the quality of images S acquired by commercial digital cameras. -
Demosaicing of Colour Images Using Pixel Level Data-Dependent Triangulation
Demosaicing of Colour Images Using Pixel Level Data-Dependent Triangulation Dan Su, Philip Willis Department of Computer Science University of Bath Bath, BA2 7AY, U.K. mapds, [email protected] Abstract G B G B G B R G R G R G Single-chip digital cameras use an array of broad- G B G B G B spectrum Charge-Coupled Devices (CCD) overlayed with a colour filter array. The filter layer consists of transpar- R G R G R G ent patches of red, green and blue, such that each CCD G B G B G B pixel captures one of these primary colours. To reconstruct R G R G R G a colour image at the full CCD resolution, the ‘missing’ primary values have to be interpolated from nearby sam- ples. We present an effective colour interpolation using a Figure 1. Bayer Colour Filter Array Pattern simple pixel level data-dependent triangulation. This inter- (U.S. Patent 3,971065, issued 1976) polation technique is applied to the commonly-used Bayer Colour Filter Array pattern. Results show that the proposed method gives superior reconstruction quality, with smaller visual defects than other methods. Furthermore, the com- and blue combined. plexity and efficiency of the proposed method is very close to Since there is only one colour primary at each position, simple bilinear interpolation, making it easy to implement we can reconstruct the image at the spatial resolution of the and fast to run. CCD only if we interpolate the two missing primary values at each pixel. That is, at a green pixel we have to gener- ate red and blue values by interpolating nearby red and blue 1 Introduction values. -
X3 Sensor Characteristics
57 J. Soc. Photogr. Sci. Technol. Japan. (2003) Vol. 66 No. 1: 57-60 Special Topic: Leading Edge of Digital Image Systems 2003 Exposition X3 Sensor Characteristics Allen RUSH* and Paul HUBEL* Abstract The X3 sensor technology has been introduced as the first semiconductor image sensor technology to measure and report 3 dis- tinct colors per pixel location. This technology provides a key solution to the challenge of capturing full color images using semi- conductor sensor technology, usually CCD's, with increasing use of CMOS. X3 sensors capture light by using three detectors embedded in silicon. By selecting the depths of the detectors, three separate color bands can be captured and subsequently read and reported as color values from the sensor. This is accomplished by taking advantage of a natural characteristic of silicon, whereby light is absorbed in silicon at depths depending on the wavelength (color) of the light. Key words: CMOS image sensor, Bayer pattern, photodiode, spatial sampling, color reconstruction, color error 1. X3 Sensor Operation ing has a beneficial perceptual effect. The net result is a series of cause-effect-remedy that adds considerable complexity and The X3 sensor is similar in design to other CMOS sensors-a cost to the capture system, while the best quality remains un- photodiode is used to collect electrons as a result of light energy achievable. entering the aperture. The charge from the photodiode is convert- By contrast, the X3 sensor reports three color values for each ed and amplified by a source follower, and subsequently read out pixel location or spatial sampling site. -
Image Demosaicing by Using Iterative Residual Interpolation
IMAGE DEMOSAICING BY USING ITERATIVE RESIDUAL INTERPOLATION Wei Ye and Kai-Kuang Ma School of Electrical and Electronic Engineering Nanyang Technological University, Singapore 639798 [email protected], [email protected] ABSTRACT A new demosaicing approach has been introduced recently, which is based on conducting interpolation on the generated residual fields rather than on the color-component difference fields as commonly practiced in most demosaicing methods. In view of its attractive performance delivered by such resid- ual interpolation (RI) strategy, a new RI-based demosaicing (a) (b) (c) method is proposed in this paper that has shown much im- Fig. 1. Comparison of color-component differences and proved performance. The key success of our approach lies in residuals: (a) a full color image, “Kodak 3”; (b) the color- that the RI process is iteratively deployed to all the three chan- component difference field, R-G; (c) the residual field, R-R.¯ nels for generating a more accurately reconstructed G chan- nel, from which the R channel and the B channel can be better Recently, a new demosaicing approach is proposed in [8], reconstructed as well. Extensive simulations conducted on which is based on the so-called residual interpolation (RI). two commonly-used test datasets have clearly demonstrated Instead of conducting interpolation on the color-component that our algorithm is superior to the existing state-of-the-art difference fields, the demosaicing algorithm as described demosaicing methods, both on objective performance evalua- in [8] performs interpolation over the residual fields. Note tion and on subjective perceptual quality. that a residual incurred at each pixel is the difference yielded Index Terms— Image demosaicing, regression filter, it- between a known color value (i.e., ground truth) and its es- erative residual interpolation. -
LMMSE Demosaicing
Color Demosaicking via Directional Linear Minimum Mean Square-Error Estimation Lei Zhang and Xiaolin Wu*, Senior Member, IEEE Dept. of Electrical and Computer Engineering, McMaster University Email: {johnray, xwu}@mail.ece.mcmaster.ca Abstract-- Digital color cameras sample scenes using a color filter array of mosaic pattern (e.g. the Bayer pattern). The demosaicking of the color samples is critical to the quality of digital photography. This paper presents a new color demosaicking technique of optimal directional filtering of the green-red and green-blue difference signals. Under the assumption that the primary difference signals (PDS) between the green and red/blue channels are low-pass, the missing green samples are adaptively estimated in both horizontal and vertical directions by the linear minimum mean square-error estimation (LMMSE) technique. These directional estimates are then optimally fused to further improve the green estimates. Finally, guided by the demosaicked full-resolution green channel, the other two color channels are reconstructed from the LMMSE filtered and fused PDS. The experimental results show that the presented color demosaicking technique significantly outperforms the existing methods both in PSNR measure and visual perception. Index Terms: Color demosaicking, Bayer color filter array, LMMSE, directional filtering. EDICS: 4-COLR, 2-COLO. *Corresponding author: the Department of Electrical and Computer Engineering, McMaster University, 1280 Main Street West, Hamilton, Ontario, Canada, L8S 4L8. Email: [email protected]. Tel: 1-905-5259140, ext 24190. This research is supported by Natural Sciences and Engineering Research Council of Canada Grants: IRCPJ 283011-01 and RGP45978-2000. 1 I. Introduction Most digital cameras capture an image with a single sensor array.