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imaging systems colleen mann

table of contents

i fundamentals ii input iii output iv process v

lambda printing

Imaging Systems




In this chapter, we will learn about different kinds of resolution. A still image is made up of three resolutions Spatial Resolution Tonal Resolution Spectral Resolution

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spatial resolution The number of pixels in the image’s X and Y dimensions make up spatial resolution. When you increase an image’s spatial resolution, there is more detail and less pixelation. These X and Y dimensions can be altered by replicating (increasing the image size) or eliminating pixels (decreasing the image size), known as interpolation.

There are multiple techniques when using interpolation. Some interpolations are faster than others, but do not necessarily reproduce satisfying images. Three common techniques for interpolation include nearest neighbor, bilinear, and bicubic interpolation.


nearest neighbor is the simplest interpolation process. It chooses the closest pixel and duplicates that pixel’s values. It is the quickest process, this file only took 2.7 seconds to process using this interpolation.

bilinear interpolation uses the 4 nearest pixel values from diagonal directions of a given pixel to find the appropriate color values of the original pixel. This file took 9.2 seconds to process using this interpolation

bicubic interpolation takes slightly longer than bilinear (9.6 seconds). However, this process considers 16 pixels that surround the original pixel, causing less interpolation artifacts. If speed is not a factor, this is the best option.

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tonal resolution

The tonal resolution of an image determines the amount of gray levels an image has. An image appears more continuous when it has more gray levels. The image pictured right visually explains how changing the tonal resolution effects the color quality of the photograph. As the tonal resolution decreases, the image quality is also sacrificed.


In the image above, the bit depth changes from 2 bits, 4 bits, 8 bits, to the original bit value of 255. Below, is the grayscale equivalent of each bit depth. In the gradients, it is more prevalent of the shift in bit depth. On the other hand, it is less noticeable in the color image.

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spectral resolution

Spectral resolution, or color resolution, makes up the channels that an image is formed. The additive colors are red, green, and blue. The RGB Color Space is used on computer monitors, cameras, and televisions.








A color image is made up of three primary channels, Red, Green, and Blue. Within the RGB channels, are subtractive channels, Cyan, Magenta, and Yellow.

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In this chapter, we will learn about the various aspects that surround the input process of photography.

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behind the image


The histogram above corresponds to the image of the pathway. According to the histogram, this image is properly exposed and is mostly made up of midtones.

The histogram above corresponds to the image of the fence. According to the histogram, this image is properly exposed, but has more shadows than it does highlights. The image also has higher contrast than the pathway image.

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camera sensors

In traditional analog photography, light sensitive emulsion captures a photograph. By exposing the film to light, a latent image is recorded on the roll of film and becomes visible to the human eye when properly developed. Color film is sensitized by red, green, and blue light. These three colors create white light.

In digital photography, the sensors are made of light-sensitive material. By using small pixels, a color image can be created. Since the human eye is twice as sensitive to green, there are two times as many green filters as there are red and blue. Light passes through the filters and onto the sensor, creating a photographic image.

color filter array


incoming light filter layer sensor array

resulting pattern

Pictured above is an example of a Color Filter Array (CFA). These filters are placed over a digital camera’s sensor. Every pixel the camera sees is captured in either red, green, or blue. CFA Interpolation allows any missing pixels from each layer to be “filled in”, known as demosaicing.

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cfa patterns

The Bayer Pattern is the most common color filter array in digital camera sensors. It is made up of 50% Green, 25% Red, and 25% Blue.

The RGBE Pattern is considered an “alternate” to the Bayer Filter. However, it is made up of Red, Green, Blue, and Emerald. This CFA can be found in Sony Cameras.

The RGBW Pattern The “W’ represents the white/transparent filter, which allows it to respond to all colors of light. The white pixels are sensitive to RGB light simultaneously.

The CYYM Pattern, unlike most CFA patterns, uses subtractive colors. It is made up of 50% Yellow, 25% Cyan, and 25% Magenta.

The CYGM Pattern uses Cyan, Yellow, Green, and Magenta. This pattern gives more luminance information and a wider dynamic range, but the colors are often inaccurate.

The RGBW Pattern by Kodak functions like the RGBW filter, but it has 50% of white/ transparent filters. Kodak called it an alternate to the Bayer Pattern.

demosaicing Demosaicing is a process used to reconstruct a full color image from the color samples from the color filter array. This interpolation is a necessary procedure in order to create a true color image. Think of demosaicing as “filling in the blanks�.


Original Image

Demosaiced Image

CFA Mosaic on Sensor

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In this chapter, we will learn about how images are processed by printers, scanners, and computers.

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digital displays and ppi

Monitors are what make digital imaging possible. Computer monitors are often referred to as the “digital darkroom�. Photographers view, process, and archive their images through these monitors and computers.

PPI refers to how many pixels per inch is in a continuous tone image. PPI strictly describes contone devices and their input resolution. These devices include computer monitor displays, scanners, and digital cameras. Each pixel provides information. Therefore, the more pixels per inch, the more information there is in an image. The higher the PPI, the highter the quality the image will be.

printers and dpi


Printers allow a photographer to make their images a physical and tangible piece of art. There are a variety of printing techniques and applications.

DPI refers to how many dots per inch is in a digitally printed photograph. When referring to an image’s DPI, you are describing the image in its binary terms- meaning how many dots make up that image. By spreading the ink on the paper, the halftone image appears continuous tone. Halftone devices such as Inkjet and Laserjet printers use DPI.

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Analog Printing

In the 1440s, Gutenberg adopted basic designs of older printing presses and made them more efficient and cost effective. Gutenberg’s invention of letterpress and movable type revolutionized printing and allowed the world to share stories, news, and communicate with one another. Since then, other analog printing techniques have been invented such as Lithography, Flexography, Gravure, and Screen printing.

Analog printing is much faster, but more expensive than Digital. Individual plates need to be made to print each page.

Digital Printing


Digital printers use a “digital plate� through their software. Since a physical plate does not need to be made for each print, costs are lower. However, the digital printing process is much slower than the analog printing process. The three kinds of digital printers include inkjet, laser, and dye sublimation. Each process is unique, but maintain the basic digital printer foundations. All digital printers use dyes or pigments to produce the text or image.

Pictured here shows what happens in the piezoelectric method, an inket process, Ink is pushed out of the cartridges and onto the paper.

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In this chapter, we will learn about the workflow layout that goes with digital photography. The image pipeline will help us better understand the functions from start to finish.


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Image Pipeline As we learned in the Output chapter, monitors and software are often referred to as the “digital darkroom”. What sets digital apart from analog is the unique and technical process an image goes through from capture to print. Below is the Image Process Pipeline, taking you step by step of a photograph’s journey in the digital darkroom.


1 Capture Image When light enters the lens, the scene is recorded on the sensor, creating a photograph.



Analog Digital Converter (ADC)

RAW Data

The ADC turns pixels into 1’s and 0’s, making the image readable by a computer and software.

1’s and 0’s are packaged into a file format known to photographers and editors as “RAW”.




CFA Interpolation

Gamma Correction

Creative Adjustments

The mosaiced information is changed from one to three monochromatic images by algorithms.

An inverse curve is applied to the image to be corrected for the human eye’s peception.

Edits such as brightness, contrast, etc. depend on the photographer’s discretion and the photograph.




RAW Processor

Noise Correction

Neutral Balance

Processors like Lightroom and Capture One are used to turn the binary numbers into color information.

Noise Correction is automatically applied when the image is opened in software.




Sharpness is necessary for printing purposes, but the necessary amount applied is subjective.



The grays (neutrals) of the photograph are corrected to prevent color casts.

Depending on the image’s use, it can be exported as a TIFF, Jpg, PSD, etc. for printing, the web, or future edits. 12 Final Product

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sharpening an image


Adobe Photoshop has an Unsharp Masking feature. Unsharp Masking is a technique wherein a blurred version of an image is subtracted from the original image and the result is added back to the original. Highlighting the edges of an image gives a perceptually sharper image.

The image pictured above is the final product of the Unsharp Mask. The edges are highlighted, making the image appear sharper.

1 Begin by blurring the image under Filter>Blur


2 Next, find the difference between the blurred image and the original image under Image>Calculations.

3 Finally, add the difference to the original file.

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lambda printing

In this chapter we will learn about a unique printing technology, lambda printing.

lambda printing

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the technology

lambda printing

Lambda Printers are an elite and unique technology. While it is a digital printer, it does not use dyes or pigments to produce the image. Within the lambda are three lasers- red, green and blue. These lasers are merged into a single beam that simultaneously exposes the digital file. This means that the image is produced in one single pass. Like the traditional darkroom, the photographic material is then developed in a “wet� film processer.

The CMYK output process is used by all inkjet printers.

Lambda Printers use RGB output, allowing for deeper blacks (D-Max Values).

lambda printing

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lambda quality Pictured here is the image at 200 pixels per inch. The Lambda Printer truly prints PPI because it is a laser technology producing the image. Inkjet printers produce images in DPI (dots per inch) because it uses dyes and pigments to produce the image.

Lambda prints are continuous tone images, while inkjet prints are halftone images.

lambda printing

Pictured here is the image at 400 pixels per inch. Lambda Printers print at true 400 PPI. The colors are more appealing and accurate. However, sharpness is an issue when printing at such a high resolution.

applications and advantages It is preferred to use a Lambda printer over an inkjet printer for backlit images. Inkjet printers give a density range of 1.8, while Lambda printers can give a density range of over 3.0. This allows the backlit image to have deeper blacks and have more contrast.

There are no size limitations when printing on Lambda. It is also much faster than inkjet printers. A Lambda printer can print at 24 inches a minute at 200 PPI and 9 inches a minute at 400 PPI, while inkjet printers can only print 1-2 inches a minute.

lambda printing

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reference materials Adobe Photoshop

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This book was created through the Imaging Systems minor with the guidance of Professor Nitin Sampat at the Rochester Institute of Technology in the fall of 2013. All photographs belong to Colleen Mann

Imaging Systems  

Created by Colleen Mann through the Imaging Systems Minor at the Rochester Institute of Technology.

Imaging Systems  

Created by Colleen Mann through the Imaging Systems Minor at the Rochester Institute of Technology.