Appendix D - Filtering in the Image Space Domain

The digital pictures were filtered using a convolution algorithm. Mathematically, a quantized image is treated as a function f (x, y). It is filtered by convolving it with a n x n matrix filter function, h(w, v). w and v are usually some small odd number: 3,5,7.. .For every pixel in f , a multiply, shift and sum operation is conducted over the range of

l = y + v
k = x + w .

and the result stored at x,y For all x,y then, the filtered image g, is given by:

.

A filter designed to sharpen an image is the simple 3x3 shown below:

.

Unfortunately, filtering real images is seldom meet with practical success with such simple schemes. The problem is that the source of the image degradations ( the blur functions) are the concatenation of camera motion, optical smearing, atmospherics, etc, and are seldom known. In theory, if one knew the blur function, one could design a filter that would remove most of the degradations. In practice, most filtering operations result in merely aesthetic improvements and no real recovery of information. The achievement of significant information recovery often entails filters, tailor made for each image being analyzed. In the course of achieving practical results, most gains in getting improvements in an image are gotten through more mundane gray shade manipulations, such as the suite of functions provided by Adobe Photoshop for contrast manipulations. Beyond this, improvements are small and tedious. The gains are usually in making subtleties noticeable. In other words, the features may seem to be suddenly “pop up” after a filtering operation. But invariably if one compares the processed image to the original, the same feature can be seen, but was overlooked due to poor contrast or smeared edges.

Appendix E - Filtering in the Image Space Domain

In the analysis of the Treblinka imagery, spatial filtering, contrast stretching and re-mappings were used extensively. The imagery was all digital. The aerial imagery had been obtained by photographing the original prints using a Nikon CoolPix 990 camera equipped with a macro lens. This camera provides high quality digital pictures. Due to the extremely close approach needed to maximize scale and resolution, lighting the subject was a great problem and extreme raking angles were entailed. This resulted in images where the pebbly surface of the print emulsions were captured. The ground photos were obtained as copies of copies from Yad Vashem, and were very degraded from the source prints.. Some pictures were downloaded from the Ghetto Fighter’s Museum’s photo archive and had been quantized to 150 bpi. All pictures required contrast stretching and re-maps. Most required some spatial filtering, if only to improve the apparent sharpness of the images. The Nikon pictures were mostly encoded with JPEG compression . This introduced degradation into the images which became very apparent after filtering, and constituted a built in limitation in resolution improvement.

We have included several examples of these processes to demonstrate the improvements achievable as well as the unavoidable limitations.


In Figure E1, the Menck Mb2 excavator was originally very dark and it was difficult to see any detail. After contrast re-map and spatial filtering, it becomes possible to see much detail. Fine features such as the cabling stand out, and the drag line attached to the clam shell, becomes visible. One can see that the sash window door is open. The SS man (probably Kurt Franz) is noticeable. The lettering on the side, however, is still illegible. Special filtering of that portion of the image yielded an increase in sharpness, as shown in Figure E2, but the original legibility is hardly improved.. Below the lettering is painted an alphanumeric. The filtered characters are crisper but the improved sharpness also makes for ambiguities: The original blurred numbers appeared to be “B. 562“. After spatial filtering the possibilities increase to four: B.553, B.552, B.562 or B.563. This exemplifies one of the factors that results from any improvement: unresolvable ambiguities also become visible.



In Figure E2 through E4, pairs of filtered and unfiltered images are shown. As one can see, the filtered pictures are quite a bit improved. Most of the improvement is aesthetic, but the details whose visibility are improved can be significant help. It should be noted that artifacts and noise are also enhanced. For example, in Figure E4, the pebbly surface is due to the low raking angles of the illumination used while photographing the original print, and to the JPEG compression used before storing the image in the Camera’s flash memory.

Index

    

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