How does high-resolution mode work?

One of the tricks of modern digital cameras is a little thing called “high-resolution mode” (HRM), which is sometimes called pixel-shift. It effectively boosts the resolution of an image, even though the number of pixels used by the camera’s sensor does not change. It can boost a 24 megapixel image into a 96 megapixel image, enabling a camera to create images at a much higher resolution than its sensor would normally be able to produce.

So how does this work?

In normal mode, using a colour filter array like Bayer, each photosite acquires one particular colour, and the final colour of each pixel in an image is achieved by means of demosaicing. The basic mechanism for HRM works through sensor-shifting (or pixel-shifting) i.e. taking a series of exposures and processing the data from the photosite array to generate a single image.

  1. An exposure is obtained with the sensor in its original position. The exposure provides the first of the RGB components for the pixel in the final image.
  2. The sensor is moved by one photosite unit in one of the four principal directions. At each original array location there is now another photosite with a different colour filter. A second exposure is made, providing the second of the components for the final pixel.
  3. Step 2 is repeated two more times, in a square movement pattern. The result is that there are four pieces of colour data for every array location: one red, one blue, and two greens.
  4. An image is generated with each RGB pixel derived from the data, the green information is derived by averaging the two green values.

No interpolation is required, and hence no demosaicing.

The basic high-resolution mode process (the arrows represent the direction the sensor shifts)

In cameras with HRM, it functions using the motors that are normally dedicated to image stabilization tasks. The motors effectively move the sensor by exactly the amount needed to shift the photosites by one whole unit. The shifting moves in such a manner that the data captured includes one Red, one Blue and two Green photosites for each pixel.

There are many benefits to this process:

  • The total amount of information is quadrupled, with each image pixel using the actual values for the colour components from the correct physical location, i.e. full RGB information, no interpolation required.
  • Quadrupling the light reaching the sensor (four exposures) should also cut the random noise in half.
  • False-colour artifacts often arising in the demosaicing process are no longer an issue.

There are also some limitations:

  • It requires a very steady scene. It doesn’t work well if the camera is on a tripod, yet there is a slight breeze, moving the leaves on a tree.
  • It can be extremely CPU-intensive to generate a HRM RAW image, and subsequently drain the battery. Some systems, like Fuji’s GFX100 uses off-camera, post-processing software to generate the RAW image.

Here are some examples of the high resolution modes offered by camera manufacturers:

  • Fujifilm – Cameras like the GFX100 (102MP) have a Pixel Shift Multi Shot mode where the camera moves the image sensor by 0.5 pixels over 16 images and composes a 400MP image (yes you read that right).
  • Olympus – Cameras like the OM-D E-M5 Mark III (20.4MP), has a High-Resolution Mode which takes 8 shots using 1 and 0.5 pixel shifts, which are merged into a 50MP image.
  • Panasonic – Cameras like the S1 (24.2MP) have a High-Resolution mode that results in 96MP images. The Panasonic S1R at 47.3MP produces 187MP images.
  • Pentax – Cameras like the K-1 II (36.4MP) use a Pixel Shift Resolution System II with a Dynamic Pixel Shift Resolution mode (for handheld shooting).
  • Sony – Cameras like the A7R IV (61MP) uses a Pixel Shift Multi Shooting mode to produce a 240MP image.

Further Reading:

Photosites – Quantum efficiency

Not every photo that makes it through the lens ends up in a photosite. The efficiency with which photosites gather incoming light photons is called its quantum efficiency (QE). The ability to gather light is determined by many factors including the micro lenses, sensor structure, and photosite size. The QE value of a sensor is a fixed value that depends largely on the chip technology of the sensor manufacturer. The QE is averaged out over the entire sensor, and is expressed as the chance that a photon will be captured and converted to an electron.

Quantum efficiency (P = Photons per μm2, e = electrons)

The QE is a fixed value and is dependent on a sensor manufacturers design choices. The QE is averaged out over the entire sensor. A sensor with an 85% QE would produce 85 electrons of signal if it were exposed to 100 photons. There is no way to effect the QE of a sensor, i.e. you can’t change things by changing the ISO.

The QE is typically 30-55% meaning 30-55% of the photons that fall on any given photosite are converted to electrons. (front illuminated sensors). In back illuminated sensors, like those typically found on smartphones, the QE is approximately 85%. The website Photons to Photos has a list of sensor characteristics for a good number of cameras. For example the sensor in my Olympus OM-D E-M5 Mark II has a supposed QE of 60%. Trying to calculate the QE of a sensor in non-trivial.

How many bits in an image?

When it comes to bits and images it can become quite confusing. For example, are JPEGs 8-bit, or 24-bit? Well they are both.

Basic bits

A bit is a binary digit, i.e. it can have a value of 0 or 1. When something is X-bit, it means that it has X binary digits, and 2X possible values. Figure 1 illustrates various values for X as grayscale tones. For example a 2-bit image will have 22, or 4 values (0,1,2,3).

Fig.1: Various bits

An 8-bit image has 28 possible values for bits – i.e. 256 values ranging from 0..255. In terms of binary values, 255 in binary is 11111111, 254 is 11111110, …, 1 is 00000001, and 0 is 00000000. Similarly, a 16-bit means there are 216 possible values, from 0..65535. The number of bits is sometimes called the bit-depth.


Images typically describe bits in terms of bits-per-pixel (BPP). For example a grayscale image may have 8-BPP, meaning each pixel can have one of 256 values from 0 (black) to 255 (white). Colour images are a little different because they are typically composed of three component images, red (R), green (G), and blue (B). Each component image has its own bit-depth. So a typical 24-bit RGB image is composed of three 8-BPP component images, i.e. 24-BPP RGB = 8-BPP (R) + 8-BPP (G) + 8-BPP (B).

The colour depth of the image is then 2563 or 16,777,216 colours (or 2563, 28=256 for each of the component images). A 48-bit RGB image contains three component images, R, G, and B, each having 16-BPP, for 248 or 281,474,976,710,656 colours.

Bits and file formats

JPEG stores images with a precision of 8-bits per component image, for a total of 24-BPP. The TIFF format supports various bit depths. There are also RGB images stored as 32-bit images. Here 8 bits are used to represent each of the RGB component images, with individual values 0-255. The remaining 8 bits are reserved for the transparency, or alpha (α) component. The transparency component represents the ability to see through a colour pixel onto the background. However only some image file formats support transparency. For example JPEG does not support transparency. Typically of the more common formats, only PNG and TIFF support transparency.

Bits and RAW

Then there are RAW images. Remember RAW images are not RGB images. They maintain the 2D array of pixel values extracted from photosite array of the camera sensor (they only become RGB after post-processing using off-camera software). Therefore they maintain the bit-depth of the camera’s ADC. Common bit depths are 12, 14, and 16. For example a camera that outputs 12-bits will have pixels in the raw image which will be 12-bits. A 12-bit image has 4096 levels of luminance per colour pixel. Once the RGB image is generated that means 4096^3 possible colours, which is 68,719,476,736 possible colours for each pixel. That’s 4096 times the amount of colours of an 8-bit per component RGB image. For example the Ricoh GR III stores its RAW images using 14-bits. This means that a RAW image has the potential of 16,384 colour for each component (once processed), versus a JPEG produced by the same camera, which only has 256 colours for each component.

Do more bits matter?

So theoretically its nice to have 68 billion odd colours, but is it practical. The HVS can distinguish between 7 and 10 million colours, so for visualization purposes 8-bits per colour component is fine. For editing an image, often the more colour depth the better. When an image has been processed it can then be stored as a 16-bit TIFF image, and JPEGs produced as needed (for applications such as the web).

From photosites to pixels (iii) – DIP

DIP is the Digital Image Processing system. Once the ADC has performed its conversion, each of the values from the photosite has been converted from a voltage to a binary number representing some value in its bit depth. So basically you have a matrix of integers representing each of the original photosites. The problem is that this is essentially a matrix of grayscale values, with each element of the matrix representing with a Red, Green of Blue pixel (basically a RAW image). If a RAW image is required, then no further processing is performed, the RAW image and its associated metadata are saved in a RAW image file format. However to obtain a colour RGB image and store it as a JPEG, further processing must be performed.

First it is necessary to perform a task called demosaicing (or demosaiking, or debayering). Demosaicing separates the red, green, and blue elements of the Bayer image into three distinct R, G, and B components. Note a colouring filtering mechanism other than Bayer may be used. The problem is that each of these layers is sparse – the green layer contains 50% green pixels, and the remainder are empty. The red and blue layers only contain 25% of red and blue pixels respectively. Values for the empty pixels are then determined using some form of interpolation algorithm. The result is an RGB image containing three layers representing red, green and blue components for each pixel in the image.

The DIP process

Next any processing related to settings in the camera are performed. For example, the Ricoh GR III has two options for noise reduction: Slow Shutter Speed NR, and High-ISO Noise Reduction. In a typical digital camera there are image processing settings such as grain effect, sharpness, noise reduction, white balance etc. (which don’t affect RAW photos). Some manufacturers also add additional effects such as art effect filters, and film simulations, which are all done within the DIP processor. Finally the RGB image image is processed to allow it to be stored as a JPEG. Some level of compression is applied, and metadata is associated with the image. The JPEG is then stored on the memory card.

From photosites to pixels (ii) – ADC

The inner workings of a camera are much more complex than most people care to know about, but everyone should have a basic understanding of how digital photographs are created.

The ADC is the Analog-to-Digital Converter. After the exposure of a picture ends, the electrons captured in each photosite are converted to a voltage. The ADC takes this analog signal as input, and classifies it into a brightness level represented by a binary number. The output from the ADC is sometimes called an ADU, or Analog-to-Digital Unit, which is a dimensionless unit of measure. The darker regions of a photographed scene will correspond to a low count of electrons, and consequently a low ADU value, while brighter regions correspond to higher ADU values.

Fig. 1: The ADC process

The value output by the ADC is limited by its resolution (or bit-depth). This is defined as the smallest incremental voltage that can be recognized by the ADC. It is usually expressed as the number of bits output by the ADC. For example a full-frame sensor with a resolution of 14 bits can convert a given analog signal to one of 214 distinct values. This means it has a tonal range of 16384 values, from 0 to 16,383 (214-1). An output value is computed based on the following formula:

ADU = (AVM / SV) × 2R

where AVM is the measured analog voltage from the photosite, SV is the system voltage, and R is the resolution of the ADC in bits. For example, for an ADC with a resolution of 8 bits, if AVM=2.7, SV=5.0, and 28, then ADU=138.

Resolution (bits)Digitizing stepsDigital values
Dynamic ranges of ADC resolution

The process is roughly illustrated in Figure 1. using a simple 3-bit, system with 23 values, 0 to 7. Note that because discrete numbers are being used to count and sample the analog signal, a stepped function is used instead of a continuous one. The deviations the stepped line makes from the linear line at each measurement is the quantization error. The process of converting from analog to digital is of course subject to some errors.

Now it’s starting to get more complicated. There are other things involved, like gain, which is the ratio applied while converting the analog voltage signal to bits. Then there is the least significant bit, which is the smallest change in signal that can be detected.

Those weird image sensor sizes

Some sensors sizes are listed as some form of inch, for example a sensor size of 1″ or 2/3”. The diagonal size of this sensor is actually only 0.43” (11mm). Cameras sensors of the “inch” type do not signify the actual diagonal size of the sensor. These sizes are actually based on old video cameras tubes where the inch measurement referred to the out diameter of the video tube. 

The world use to use vacuum tubes for a lot of things, i.e. far beyond just the early computers. Video cameras like those used on NASA’s unmanned deep space probes like Mariner used vacuum tubes as their image sensors. These were known as vidicon tubes, basically a video camera tube design in which the target material is a photoconductor. There were a number of branded versions, e.g. Plumicon (Philips), Trinicon (Sony).

A sample of the 1″ vidicon tube, and its active area.

These video tubes were described using the outside diameter of the overall glass tube, and always expressed in inches. This differed from the area of the actual imaging sensor, which was typically two-thirds of the size. For example, a 1″ sized tube typically had a picture area of about 2/3″ on the diagonal, or roughly 16mm. For example, Toshiba produced Vidicon tubes in sizes of 2/3″, 1″, 1.2″ and 1.5″.

These vacuum tube based sensors are long gone, yet some manufacturers still use this deception to make tiny sensors seem larger than they are. 

Image sensorImage sensor sizeDiagonalSurface Area
Various weird sensor sizes

For example, a smartphone may have a camera with a sensor size of 1/3.6″. How does it get this? The actual sensor will be approximately 4×3mm in size, with a diagonal of 5mm. This 5mm is multiplied by 3/2 giving 7.5mm (0.295″). 1” sensors are somewhere around 13.2×8.8mm in size with a diagonal of 15.86mm. So 15.86×3/2=23.79mm (0.94″), which is conveniently rounded up to 1″. The phrase “1 inch” makes it seem like the sensor is almost as big as a FF sensor, but in reality they are nowhere near the size. 

Various sensors and their fractional “video tube” dimensions.

Supposedly this is also where MFT gets its 4/3 from. The MFT sensor is 17.3×13mm, with a diagonal of 21.64mm. So 21.64×3/2=32.46mm, or 1.28″, roughly equating to 4/3″. Although other stores say 4/3 is all about the aspect ratio of the sensor, 4:3.

Photosites – Well capacity

When photons (light) enter a lens of a camera, some of them will pass through all the way to the sensor, and some of those photons will pass through various layers (e.g. filters) and end up in being gathered in the photosite. Each photosite on a sensor has a capacity associated with it. This is normally known as the photosite well capacity (sometimes called the well depth, or saturation capacity). It is a measure of the amount of light that can be recorded before the photosite becomes saturated (no long able to collect any more photons).

When photons hit the photo-receptive photosite, they are converted to electrons. The more photons that hit a photosite, the more the photosite cavity begins to fill up. After the exposure has ended, the amount of electrons in each photosite is read, and the photosite is cleared to prepare for the next frame. The number of electrons counted determines the intensity value of that pixel in the resulting image. The gathered electrons create a voltage which is an analog signal -the more photons that strike a photosite, the higher the voltage.

More light means a greater response from the photosite. At some point the photosite will not be able to register any more light because it is at capacity. Once a photosite is full, it cannot hold any more electrons, and any further incoming photons are discarded, and lost. This means the photosite has become saturated.

Fig.1: Well-depth illustrated with P representing photons, and e- representing electrons.

Different sensors can have photosites with different well-depths, which affects how many electrons the photosite can hold. For example consider two photosites from different sensors. One has a well-depth of 1000 electrons, and the other 500 electrons. If everything remains constant from the perspective of camera settings, noise etc., then over an exposure time the photosite with the smaller well-depth will fill to capacity sooner. If over the course of an exposure 750 photons are converted to electrons in each of the photosites, then the photosite with a well-depth of 1000 will be 75% capacity, and the photosite with a well-depth of 500 will become saturated, discarding 250 of the photons (see Figure 2).

Fig.2: Different well capacities exposed to 750 photons

Two photosite cavities with the same well-capacities, but differing size (in μm) will also affect how quickly the cavity fills up with electrons. The larger sized photosite will fill up quicker. Figure 3 shows four differing sensors, each with a different photosite pitch, and well capacity (the area of each box abstractly represents the well capacity of the photosite in relation to the photosite pitch).

Fig.3: Examples of well capacity in various sensors

Of course the reality is that electrons do not need a physical “bin” to be stored in, the photosites are just shown in this manner to illustrate a concept. In fact the concept of well-depth is somewhat ill-termed, as it does not take into account the surface area of the photosite.

From photosites to pixels (i) – the process

We have talked briefly about digital camera sensors work from the perspective of photosites, and digital ISO, but what happens after the light photons are absorbed by the photosites on the sensor? How are image pixels created? This series of posts will try and demystify some of the inner workings of a digital camera, in a way that is understandable.

A camera sensor is typically made up of millions of cavities called photosites (not pixels, they are not pixels until they are transformed from analog to digital values). A 24MP sensor has 24 million photosites, typically arranged in the form of a matrix, 6000 pixels wide by 4000 pixel high. Each photosite has a single photodiode which records a luminance value. Light photons enter the lens and pass through the lens aperture before a portion of light is allowed through to the camera sensor when the shutter is activated at the start of the exposure. Once the photons hit the sensor surface they pass through a micro-lens attached to the receiving surface of each of the photosites, which helps direct the photons into the photosite, and then through a colour filter (e.g. Bayer), used to help determine the colour of pixel in an image. A red filter allows red light to be captured, green allows green to be captured and blue allow blue light in.

Every photosite holds a specific number of photons (sometimes called the well depth). When the exposure is complete, the shutter closes, and the photodiode gathers the photons, converting them into an electrical charge, i.e. electrons. The strength of the electrical signal is based on how many photons were captured by the photosite. This signal then passes through the ISO amplifier, which makes adjustments to the signal based on ISO settings. The ISO uses a conversion factor, “M” (Multiplier) to multiply the tally of electrons based on the ISO setting of the camera. For higher ISO, M will be higher, requiring fewer electrons.

Photosite to pixel

The analog signal then passes on to the ADC, which is a chip that performs the role of analog-to-digital converter. The ADC converts the analog signals into discrete digital values (basically pixels). It takes the analog signals as input, and classifies them into a brightness level (basically a matrix of pixels). The darker regions of a photographed scene will correspond to a low count of electrons, and consequently a low ADU value, while brighter regions correspond to higher ADU values. At this point the image can follow one (or both) of two paths. If the camera is set to RAW, then information about the image, e.g. camera settings, etc. (the metadata) is added and the image is saved in RAW format to the memory card. If the setting is RAW+JPEG, or JPEG, then some further processing may be performed by way of the DIP system.

The “pixels” passes to the DIP system, short for Digital Image Processing. Here demosaicing is applied, which basically converts the pixels in the matrix into an RGB image. Other image processing techniques can also be applied based on particular camera settings, e.g. image sharpening, noise reduction, etc. is basically an image. The colour space specified in the camera is applied, before the image as well as its associated meta-data is converted to JPEG format and saved on the memory card.

Summary: A number of photons absorbed by a photosite during exposure time creates a number of electrons which form a charge that is converted by a capacitor to a voltage which is then amplified, and digitized resulting in a digital grayscale value. Three layers of these grayscale values form the Red, Green, and Blue components of a colour image.

How does digital ISO work?

The term ISO (International Standards Organization) is used to describe light sensitivity. In the world of film, ISO relates to film sensitivity – film with high ISO is made with crystals capable of holding more light. The trade-off is that the crystals need to be larger, therefore as ISO increases crystal size becomes more visible, manifesting as film grain. In the digital realm, photosites cannot increase in size, so in low light they record less information. To compensate for a lack of information, the signal is amplified, thereby mimicking film sensitivity.

A low ISO (e.g. 100) setting mimics a low-sensitivity film, so that a longer exposure time, or large aperture setting is required. Conversely a high ISO setting, e.g. 1600, mimics a high-sensitivity film, so allows for a short exposure time (fast shutter speed), or small aperture. Increasing the ISO setting will effectively increase the brightness of the resulting image. Note that changing the ISO has nothing to do with the sensitivity of the photosites, they are by no means affected. This is different to film cameras, where changing the ISO setting is directly associated with the sensitivity of the film. The ISO in a digital camera has everything to do with what happens to the signal after it has been captured by the photosite and converted from light to an electrical signal. The ISO setting determines what happens when the electrical signal passes through an analog amplifier, i.e. it determines how much the signal is amplified (this is known as the gain).

Digital ISO

A brightly lit scene will produce a strong electrical signal, which requires less amplification (lower ISO setting), and results in a smoother image with less “grain”. Conversely, less light in a scene means photosites are able to capture less information, and generate weaker electrical signals which have to be amplified (using a high ISO setting). Unfortunately, photosites also capture noise, and changing the ISO will also affect it. For example increasing ISO will increase the amount of noise. This is why photographs taken with a high ISO often have a grainy appearance (attributable to noise). The lower the ISO used, the better the quality of the image will be.

Why do buildings lean? (the keystone effect)

Some types of photography lend themselves to inherent distortions in the photograph, most notably those related to architectural photography. The most prominent of these is the keystone effect, a form of perspective distortion which is caused by shooting a subject at an extreme angle, which results in converging vertical (and also horizontal) lines. The name is derived from the archetypal shape of the distortion, which is similar to a keystone, the wedge-shaped stone at the apex of a masonry arch.

keystone effect in buildings
Fig.1: The keystone effect

The most common form of keystone effect is a vertical distortion. It is most obvious when photographing man-made objects with straight edges, like buildings. If the object is taller than the photographer, then an attempt will be made to fit the entire object into the frame, typically by tilting the camera. This causes vertical lines that seem parallel to the human visual system to converge at the top of the photograph (vertical convergence). In photographs containing tall linear structures, it appears as though they are “falling” or “leaning” within the picture. The keystone effect becomes very pronounced with wide-angle lenses.

Fig.2: Why the keystone effect occurs

Why does it occur? Lenses are designed to show straight lines, but only if the camera is pointed directly at the object being photographed, such that the object and image plane are parallel. As soon as a camera is tilted, the distance between the image plane and the object is no longer uniform at all points. In Fig.2, two examples are shown. The left example shows a typical scenario where a camera is pointed at an angle towards a building so that the entire building is in the frame. The angle of both the image plane and the lens plane are different to the vertical plane of the building, and so the base of the building appears closer to the image plane than the top, resulting in a skewed building in the resulting image. Conversely the right example shows an image being taken with the image plane parallel to the vertical plane of the building, at the mid-point. This is illustrated further in Fig.3.

Fig.3: Various perspectives of a building

There are a number of ways of alleviating the keystone effect. The first method involves the use of specialized perspective control and tilt-shift lenses. The best way to avoid the keystone effect is to move further back from the subject, with the reduced angle resulting in straighter lines. The effects of this perspective distortion can be removed through a process known as keystone correction, or keystoning. This can be achieved in-camera using the cameras proprietary software, before the shot is taken, or in post processing on mobile devices using apps such as SKRWT. It is also possible to perform the correction with post-processing using software such as Photoshop.

Fig.4: Various keystone effects