.

What In The World Is Detail Frequency ?

refraction73_MG_1171

High frequency

refraction74_20100809__ICEjokulsarlon_0148

Medium Frequency

refraction6420080816_iceland_reykjanes_0496

Low Frequency

Frequency is a term that’s being used more and more. That’s because new tools offer you more control over frequency than ever before. Noise reduction, sharpening, and HDR all offer unprecedented control over the look and feel of detail in our images. Frequency is used to describe the amount of detail packed into a given area of an image. This is measured by the amount of tonal variation between rows or columns of pixels. Imagine measuring an image with a line that passes across it (horizontally from left to right or vertically from top to bottom). The mean or average tonal value along lines can be charted and then compared to values from other measurement lines, especially those nearest to each other.
Read More

3 Things Creative Sharpening Can Do For Your Images

Refraction LXIX 2
Of the three stages in a sound sharpening workflow – capture sharpening, creative sharpening, and output sharpening – creative sharpening is the stage that has the most impact.
The goal of creative sharpening is to give an image a specific look and feel. There are at least three things creative sharpening can do for your images. One, creative sharpening can prioritize; it can direct attention to specific areas of an image. Two, creative sharpening can enhance; qualitative aspects of images like texture and line, can be amplified to produce stronger responses. Three, creative sharpening can be used to accentuate different qualities of light; a great deal of detail is carried by the luminosity component of color and changing it changes the overall appearance of light within the image. Used consistently creative sharpening can produce a distinctive style that is more easily recognizable to viewers. (Remember, sharpening is a way to enhance details and it may also be used with its counterpart blurring to make effects appear even stronger by comparison.)
Read More

Creative Image Sharpening – Double Pass Versus Hybrid

1_Sharpen_No

Before sharpening

2_Double_USM

Unsharp Mask only

3_Hybrid_USMHighPass

Unsharp Mask and High Pass filters combined

Different sharpening techniques make the world look different. A world of difference can be seen between the thin hard line of Unsharp Mask and the broad feathered line of High Pass Sharpening.
Can you choose a combination of both? Yes you can! You can choose the texture of one, the halo of another, and the line of yet another, applying them either globally or selectively. You can customize the look and feel of detail in any image or image area with astonishing precision and flexibility.
Read More

The Advantages Of Sharpening With Photoshop's Layers

1_sharp_refraction_LXXIII1 copy

2_sharpenedlayerstack copy

Layers have Blend Modes and can be masked

3_layerstyle copy

Double click a layer to activate its Blend If sliders  

There are many reasons to use layers when sharpening your digital images.
How do you do this? Simply duplicate the Background layer and sharpen the new layer.
Eliminate Saturation Shifts
Layers can be used to eliminate saturation shifts. Change the Blend Mode of a sharpening layer from Normal to Luminosity. Color noise will also be reduced this way.
Read More

36 Great Quotes On Looking

Quotes_Looking
Enjoy this collection of quotes on Looking.
“All of us are watchers – of television, of time clocks, of traffic on the freeway – but few are observers. Everyone is looking, not many are seeing.” – Peter M. Leschak
“We do a lot of looking: we look through lenses, telescopes, television tubes… Our looking is perfected every day, but we see less and less.” – Frederick Franck
“Seeing, in the finest and broadest sense, means using your senses, your intellect, and your emotions. It means encountering your subject matter with your whole being. It means looking beyond the labels of things and discovering the remarkable world around you.” – Freeman Patterson
“Joy in looking and comprehending is nature’s most beautiful gift.” – Albert Einstein
“All of the top achievers I know are life-long learners… Looking for new skills, insights, and ideas. If they’re not learning, they’re not growing … not moving toward excellence.” – Denis Waitley
“Thinking is more interesting than knowing, but less interesting than looking” – Johann Wolfgang von Goethe
“We learn more by looking for the answer to a question and not finding it than we do from learning the answer itself.” – Lloyd Alexander
“Poetry surrounds us everywhere, but putting it on paper is, alas, not so easy as looking at it.” – Vincent van Gogh
Read More

How To Avoid 3 Types & 2 Kinds Of Noise

Reflection XII Adagio

high noise / low noise

noise_banding

column and row noise / severe under-exposure

noise_fixedpattern

hot pixel noise / very long exposure or very hot conditions

noise_bayer

Bayer pattern noise / substantial under-exposure

noise_random

random noise / high ISO

While it’s best to eliminate noise in images at the point of capture (by choosing optimum tools and making exposures), taking the steps to do this may be impractical and/or lead to unacceptable trade-offs, so you may need to make a compromise settling for reducing it (first during exposure and second during post-processing). But which compromises should you make? Knowing the types of noise that are produced in digital images and how they are produced will help guide you to solutions that will eliminate, reduce, or remove it.

There are three types of noise; random noise, fixed pattern noise, and banding noise.

Random Noise

Random noise appears as both luminance (light and dark) and chrominance (hue/saturation) variations not native to an image but produced by the electrical operation of a capture device. The electrical signal produced in response to photons is comingled with electrical variations in the operation of the capture device. Random noise patterns always change, even if exposure conditions are identical. Random noise is most sensitive to ISO setting. Again, digital cameras have one native ISO setting; higher ISO settings artificially boost the signal produced by the sensor and the noise accompanying it. The results? You get a brighter picture from less light and exaggerated noise. Since the pattern is random it is challenging to separate the noise from the image, especially texture, and even the best software used to reduce it through blurring may compromise image sharpness; how much depends on the level of reduction.

Fixed Pattern Noise

Fixed pattern noise (“hot pixels”) is a consistent pattern specific to an individual sensor. Fixed pattern noise becomes more pronounced with longer exposures. Higher temperatures also intensify it. Since the pattern is consistent, it can be easily mapped and reduced or eliminated.

Column & Row Noise

Banded noise is introduced with the camera reads the data produced by the sensor. It’s camera-dependent. Banding noise is most visible at high ISOs, in shadows, and when an image has been dramatically brightened. This type of noise quickly becomes obvious and objectionable; the regular row and column patterns from the sensor quickly call attention to the capture device; it is challenging to reduce without severely compromising image sharpness.

Noise can be broken down into two kinds; chromatic (hue/saturation variances) and luminance (brightness variances).

noise_chrominance

chrominance / color noise

Chromatic Noise

Chromatic noise produces a more ‘unnatural’ appearance, it is easier to reduce without compromising image sharpness than luminance noise. Chromatic blurring is less noticeable than luminance blurring, as human perception tends to see color contained within contours, even when it is not precisely true. It’s a convenient optical illusion. Larger chromatic variances may result from bayer pattern demosaicing. (Digital sensors typically capture photons with an array of two green, one red, and one blue photosites that register separate luminance values for each site. This data is then processed, ‘averaged’ if you will, to generate a final color, such as brown or lavender, or even a specific green, red, or blue. If done under suboptimal conditions, such as underexposure, larger areas of color variances may occur and will require additional post-processing. Extreme amounts of chromatic noise reduction may results in reduced saturation, especially along contours separating strongly contrasting colors.

noise_luminance

luminance / light & dark noise

Luminance Noise

The presence of luminance noise is more readily accepted than chrominance noise. Luminance noise is harder to reduce than chrominance noise. Luminance information encodes contour, volume, and texture, key elements in representational images. Aggressive amounts of luminance noise reduction subdue image texture, creating a synthetic or overly smooth appearance, and blurs contours, lessening the appearance of focus. Camera noise reduction tends to be crude. Raw conversion software produces significantly improved results. For more extreme noise reduction, third-party software (such as Define, NoiseNinja, and NoiseWare) offer superior functionality and results.

Noise also varies in both magnitude and spatial frequency. Noise occurring over short distances has a high frequency (it’s ‘fine-grained’), while noise occurring over long distances has a low frequency. Noise magnitude, often described by the statistical measure of ‘standard deviation’, quantifies the variance a pixel will have from its ‘true’ value. Higher magnitude noise overpowers fine texture and becomes exceptionally difficult to remove.

The noise floor dominates other forms of noise. It is created by the type of read circuits in the sensor, the transistor characteristics, and support circuits such as the analog to digital converter. As light levels increase the noise associated with light (‘photon shot noise’) exceeds the noise floor. If the signal is increased by a factor of two (one f-stop), then the noise increases by a factor of one and the signal to noise ration increases by one. A higher signal to noise ratio makes noise less visible. When the signal exceeds the maximum value the sensor is capable of capturing (dynamic range is a measure of the largest ratio of the capture signal to the noise floor), the noise drops because the signal is pinned at the saturation value.

With a thorough understanding of what produces noise, how it is produced, what kinds and types of noise to be on the lookout for, you can take steps to reduce it at the point of capture. You want to start with as little noise as possible. If you want noise, you can always add it later, which gives you the possibility of customizing it with almost infinite precision. If you begin with noise in your originals, you’re locked in, and it can be challenging to reduce it without compromising image quality – sharpness, texture, saturation, and hue variety. Given that noise isn’t the only concern you balance, for some uses this may be an acceptable trade-off.

.
.

5 Great Ways To Eliminate Image Noise At The Source

Reflection XXXVII

high noise / low noise

Noise happens. There is always some degree of noise present in any electronic device that receives or transmits a signal. Though noise is unavoidable, it can become so small relative to the signal that it is no longer visible. It’s all about a good signal to noise ration (SNR). The image is the signal. The capture and carrier mediums’ biproducts are the noise. (Film grain is noise.)The higher the SNR the more the image overpowers the noise; the lower the SNR the more the image becomes confused with noise. While some noise increases the apparent sharpness of images, the vast majority of noise found in images degrades quality. It’s best to avoid it.
There are a number of things you can do to reduce noise in your digital images at the point of capture.
1       Expose to the right
2       Use lower ISO settings
3       Use bigger sensors
4       Use faster exposure times
5       Keep equipment cool
Start here. Use software to reduce the appearance of noise only when needed. (Of course, reducing noise may not be the only deciding factor when selecting conditions, tools, and techniques. As with all things, you will have to balance many concerns simultaneously. Adhere to the above principles when practical.)
Knowing how noise is produced will help you avoid it.
histograms_zones_caution

avoid the red zone, caution in the yellow zone, go for the green zone

ETTR
Underexposure results in more visible noise. Darker regions contain more noise than lighter regions in digital capture; the opposite is true of film. This is because the darker regions are recorded with fewer photons and less bits of data. It stands to reason that darker regions of images are produced with fewer photons. But why are they recorded with less bits of data? Digital cameras record data in a linear progression. If a digital sensor is capable of recording 14 bits of data (or 16,384 shades of gray) with a dynamic range of 8 stops, the lightest stop contains half the data in the file; the next lightest stop contains half as much data; and so on; it generates this progression from dark to light – 32 / 64 / 128 / 512 / 1024 / 2048 / 4096 / 8,192. This means you want to expose to the right. Avoid the lower 2 stops whenever possible; they contain less than 1% of the total data in the file. With so little data the signal becomes confused with the noise; the signal to noise ratio is very low. What’s more, when these tonal regions are adjusted, brighter or contrastier, the noise contained there quickly becomes more pronounced and with so few bits of data it also has a tendency to posterize. In addition, significant under exposure greatly increases the chances of producing Bayer pattern noise, a type of noise that is seen in larger areas and is more challenging to remove in post-processing. You’re much better off making a light exposure and darkening the Raw file during post-processing; this way your shadows will be represented with much more data and contain less noise. When light becomes challenging, which should you choose – under expose (without clipping) or raise the ISO? The precise cut off point is different for every camera, but in general avoid the lower 20% of the histogram. You’ll get less noise if you boost ISO and move the histogram up.
ISO
Higher ISOs amplify noise. ISO (International Standards Organization) is a descriptor that signifies absolute sensitivity to light. A digital sensor has one native ISO. Higher ISO settings simply boost the resulting signal. This is useful, but not ideal. When the brightness of the image is boosted, the noise is too.
Use “Shorter” Exposure Times
Longer exposures generate more noise. Hot pixels become hotter. All sensors have a few pixels that heat up faster than others, producing brighter than expected values. Some even have a few dead pixels that never fire, producing only black pixels. During longer exposures hot pixels are given more opportunity to heat up, growing brighter still; slightly hot pixels not visible at shorter exposure times become visible. As digital sensors age, hot pixels may become hotter and more pixels may become hot. Hot pixels produce a consistent fixed pattern of noise that can be recorded for given exposure times, making it easy to reduce. There is a duration at which a sensor suddenly produces a lot more noise; in older models this could be seen in exposures lasting only one minute, but with newer sensors this is greatly extended and most users will not encounter this; if you’re making very long exposures it’s useful test this and find the duration at which this happens for a your specific camera.
Use Bigger Sensor Sites
When it comes to noise, bigger is better – theoretically. Bigger sensors have more light gathering capacity, producing a higher signal to noise ratio, or cleaner images. More isn’t necessarily better. Cameras with more photosites (yielding more megapixels) packed into smaller areas tend to produce a lower signal to noise ratio, or noisier images. That said, a stronger signal does not necessarily guarantee lower noise. It’s the relative amounts of signal to noise that determines how noisy an image appears. The way a camera processes the file it makes has a significant impact on the final quality. Consequently, many medium format cameras that produce beautiful files in daylight typically produce noisier files than DSLR’s with smaller sensors in low light.
Stay Cool
High temperatures exacerbate noise. Thermal energy (leakage current) in semiconductors can generate an electrical signal that is difficult to distinguish from the optical signal. Ambient temperature increases leakage current by a factor of 2 for every 8 degrees Centigrade. Whether due to ambient temperature (You might start to see some effect over 90 F / 32 C degrees.) or storage (Don’t leave your camera in the sun, especially on a car seat, for long periods of time.), your camera can get hot and this will increase noise, which you can reduce by cooling off and keeping your camera cooler.
The best way to avoid noise is to produce as little of it as possible during exposure. These five tips will help you do just that.

.
.