Outlining Some Imagery Enhancement Principles and Introducing the Spatial Domain (ii)

In the first part of this article we looked at the concept of resolution from principally the radiometric (or “gray level”) aspect of imagery. It was demonstrated how this could be made more interpretable by enhancing information contained within the image that was not normally perceivable by the observer. The concept of resolution in imagery interpretation is multi–dimensional. The axes of resolution of imagery also spread across the spectral domain ( “color”) , the temporal domain (the time interval(s) between images being captured) as well as the spatial ( “detail”) domain which we introduced at the end of (i) . Incidentally this last domain is often commonly and incorrectly touted as “ the resolution” of imagery, in disregard of the other three imagery resolution domains. The important concept of spatial frequency outlined in (i) enables us to address characteristics contained within the spatial domain of an image. To recap:

Fig. 1.Low Spatial Frequency Component (a) across “x” axis of image e.g. cloud (b)

This representation of a spatial frequency component shows a typical low frequency sine wave typified by gradual changes across the image, such as with overcast cloud. The sine wave pattern shown above can be expressed in a simple concept that involves three possible variables, The spatial frequency, the amplitude (positive or negative), and the phase.

Fig.2 . High Spatial Frequency Component (a) across “x” axis of image e.g. pebbles (b).

In contrast this spatial frequency component typifies a more densely packed variation in radiometry across the image .The representation of an image in terms of all the spatial frequencies contained within it brings in a branch of mathematics that enables us to transform the representation of an image from the normally perceived spatial domain ( i.e. “picture”) across to the frequency domain. The formula by which we convert, or transform, an image from one domain to the other is referred to as a domain transform and the most commonly used of these is the Fourier Transform .

Joseph Fourier (1768-1830) was a French physicist who developed a range of functions , based upon frequency, which are used in many branches of mathematics and science. In digital imagery the image can be seen as a function of two variables ( the pixel value is the function, its “x” and “y” co-ordinates the two variables) .The Fourier transform can display a new representation of an image, based upon the spatial frequency components, and preserving all the information in the original image. Complicated functions can be represented in this way and worked upon in an easier manner.

We can represent these frequencies graphically by re displaying the image information in terms of the spatial frequencies contained within it in the frequency domain , rather than as a pixel matrix in the spatial domain..

So, for our first case above, the low spatial frequency across the image can be represented as seen below:

Fig.3. Low spatial frequency (a) across image “x” axis expressed as a frequency component (b).

Note that the center point –the origin ( which actually represents the mean brightness level or signal amplitude in the image) is bracketed by two points close in, i.e. indicating a low spatial frequency . This pair of points represents a particular spatial frequency in the image along the “x” axis. Also note that these two points are symmetrical in distance from the origin. This opposing symmetry of frequencies always appears in an image expressed in spatial frequency terms.

It is perhaps helpful to note at this point that this is not just abstract theory. If a lens has a slide of Fig. 3(a) above placed at its focal length and a piece of frosted glass put at its focal plane then a beam of coherent ( i.e. laser) light shone through the lens will produce an image identical to that at Fig. 3 (b) above on the glass. The lens is actually performing the transform operation optically.

Fig. 4. Higher Spatial frequency ( a) in “x” axis i.e. across image , expressed as a frequency component ( b).

Do note that in Fig.4. above the two points are more displaced outwards towards the left and right edges then in Fig. 3.,being representative of a higher spatial frequency.

So far we have only looked across the image. If we now rotate 90 deg. and look up and down the image’s spatial frequencies, precisely the same transformation occurs in the “y” axis , we just have to rotate everything accordingly thus:

Fig. 5. Spatial Frequency in “y” axis i.e. up and down image (a) expressed as a frequency component ( b).

Fig. 6. The same rules apply through 360 degrees in the plane of the image .

So, if we add all the of spatial frequencies in all orientations in the x,y, plane to get the Fourier transform of an image , we typically end up with an expression of the image in the frequency domain as looking something like this:

.

Fig.7. Typical Fourier Transform of an image.

This representation of an image in the frequency domain contains all the original spatial information. Indeed if the Fourier transform is inverted ( i.e. “reversed” ) the original image will be faithfully reconstructed in the spatial domain . The original picture information is all intact. Note the brightness along the “x” ( horizontal ) and “y” ( vertical axes) these indicate an image with distinct vertical and horizontal features, such as one might see in doors or windows for example.

In the transform the low spatial frequencies are towards the central origin and the higher spatial frequencies ranging out to periphery. So if ,for example, we wish to reduce high spatial frequency “noise” we can remove the outer parts of this frequency domain transform before running the inverse transform. Thus an image can be “smoothed”. As we are letting the low spatial frequencies dominate in the image, this process is often referred to as “low-pass” filtering.

Fig 8. ” Low Pass” Fourier filter.

Conversely removing lower frequencies close to the center will “sharpen” the image for the observer. (a.k.a.” High Pass” Filtering)

Fig.9. “High Pass” Fourier filter.

So, to recap, the various changes in radiometry across the image in all directions are open to be detected and measured in terms of spatial frequency . In a manner conceptually somewhat analogous to that used to group pixels by their radiometry (as outlined in the earlier paper) instead of their physical positions (x,y,) in an image.

The spatial frequency components of an image can be re-plotted from low to high on new “x” and “y” axes across the frequency range. Although looking nothing like a “picture” all the information in the original image is retained and the image can be reconstructed back in to the spatial domain by reversing the process. The varying spatial frequencies can be attenuated in order to make the image more interpretable , however no additional external information is added to the image.

However it is not necessary to always move an image from the spatial domain to the frequency domain in order to perform filtering operations . The image can be directly and specifically filtered in the spatial domain by a process known as image convolution. This operation, in essence, looks at the relationship of a pixel to those pixels around it. It is often referred to as a neighborhood operation.

In this routine each pixel is systematically looked at in turn and its relationship with the pixels around it weighted. The end result being an image that can be high pass, low pass or edge enhanced in a number of ways. The mathematical instrument that looks at and adjusts this relationship is called a convolution kernel or, alternatively, a “mask” in some texts.

Here is a schematic diagram of a typical convolution kernel or “mask” .

Fig.10. A typical convolution kernel.

Several points can be noted. This kernel covers a 9 element matrix of 3x3 pixels centered on the pixel being weighted at value 16 in this example – which is of a typical high pass filter incidentally. Many convolution kernels are of this size for a number of reasons, primarily because the small size is easier to implement compared to bigger kernels, e.g. 7x7, 9x9 up to 25x25 or more for some specialist low-pass operations that generate exponentially greater number of calculations: hence the practicability of working in the Fourier domain for some complex tasks. However, some of the bigger sizes can be approximated by performing a series of 3x3 convolutions.

The convolution kernel scans the image from top left , pixel by pixel, row by row as can be conceptually visualized below.

Fig 11. Schematic of Convolution Kernel Scanning an image Pixel Matrix.

The convolution kernel addresses each image pixel value in turn as it reads across the rows and down the columns of the image. Above we can see that is centered on a pixel with a gray level value of “8”. We’re using single digit figures here to keep the math in check, incidentally.

The input value for the pixel is “8”. Now each of the nine pixels covered in the original matrix is first of all multiplied by the weighting value in its corresponding position in the kernel . The top left pixel in the grayed out section of the image has the value 8. This is multiplied by the top left value in the kernel which is “-1” .So (-1 ) x 8 = -8 .

The kernel then adds all of these values up for the nine pixels covered as shown schematically below:

(-1x8) + (-1x2) + ( -1x2)

+

(-1x6) + (16x8) + (-1 x2)

+

(-1x6) + (-1x6) + (-1x8)

The sum total of all these values is (128- 40 ) i.e. 88

This kernel then divides this figure by the sum of all the values contained within the kernel itself i.e.( 16-8) = 8

88/8 = 11

“11” is then inserted in to the output image in the place of the original pixel value of “8” and the kernel moves on to the next pixel in the original image and repeats the process. .

This is a typical example of a high frequency pass kernel, in which relatively lower values become lower whilst the higher values become higher. A kernel with these characteristics reversed will act as a low pass or “smoothing” filter.

It might be helpful now to look at a couple of image enhancements in order to illustrate these operations in practice .

We can start by looking at a copy of one of the most famous “ UFO” images of the ‘50’s ( and the very first one seen by the author at a tender age).

Fig.11. George Adamski “ Flying Saucer”.

If this image is high pass filtered by a convolution kernel similar to the one used in the example above, a more “crisp“ image emerges.

Fig 12 .Original Adamski Image (a). High pass filtered (b).

Note, however, that although the rim of the saucer and the edges of the apparent windows appear sharper the “noise” in the image is also more apparent, for example in the background area towards the lower left of the picture. This process is somewhat analogous to adjusting the bass and treble output on a music sound system. The desired result can be heard, but all the original information is still there in the recording.

A typical edge enhancement convolution kernel can bring out features as illustrated below:

Fig 13 .Original Adamski Image (a). Edge enhancement filtered ( b)

In practice the edge enhancement routine is usually added back in to the original image in many cases to improve its appearance for many applications, ranging from imaging satellites to personal digital cameras.

In a future article we will look at some practical examples of the application of imagery enhancement techniques in UFO imagery.