However, interpolation will not give you new data it will only make exsting data smoother. In that respect you are decreasing the pixel distance when you interpolate. If you upsample (aka interpolate, or scale) the image array from $M,n$ to, say, $3M, 3N$, then the displayed image size will also be tripled.Īnd finally interpolation of an image (or any digital data) means computing the sigal values that's physically in between its existing samples. $$ \text $$ vertically in inches.įor amonitor with a given DPI, an image with smaller array size $M,N$ will display smaller on the screen. Assuming square pixels, DPI of a physical display device is computed by In those examples from Gonzalez's book, images are considered with their physical sizes when displayed (or printed). Some programs try to keep image physical display size the same, while scaling them or when displayed on different monitors, by incorporating a dpi parameter to be used as well.
This is especially important on text processing with font rendering. When you display a scaled image on the same physical monitor, then the apparent dpi of the image content will be changing too.
It just upsamples or downsamples the image array.ĭPI (Dot Per Inch) is considered only when the actual physical size of the displayed (or printed) image is of concern. If the specified size does not produce the same aspect ratio as the input image, the output image will be distorted. Pass imresize a vector that contains the number of rows and columns in the output image. This function creates more pixels using the pixel values already present in the neighborhood. Resize the image again, this time specifying the desired size of the output image, rather than a magnification value. For example, if we resize an image two times of size 100-by-100, then its final size will be 200-by-200. This function resizes images by increasing or decreasing their pixels. The custom interpolation result does not exhibit a ringing artifact.In digital image processing, an image resizing changes only the array size (dimensions) of the image. We can use the imresize () function to resize images in Matlab.
The custom interpolation result (lower right) is slightly sharper than the bicubic and lanczos2 results with slightly smoother diagonal edges. The ringing artifact appears as a faint echo outside the gray boundary, or by looking just to the left and right of the thick black stripe running down the middle of the image. The lanczos3 result (lower center) is sharper than the bicubic and lanczos2 results but exhibits a visible "ringing" artifact. For example, look closely at the digits "3" and "8" near the top of the image. The names of the saved & original images must stay the same if that makes any difference. The bicubic result (upper right) and lanczos2 result (lower left) appear very similar and are sharper than the bilinear result. I am able to get matlab to resize and save an individual image, but I'm wondering how I can get it to do this for all images in a folder without having to type in the names one by one. The bilinear result (upper center) is better in most respects than the nearest-neighbor result but looks a little blurry. The nearest-neighbor result (upper left) appears quite blocky. B_osc = the resized images as a tiled image, and compare the results subjectively.
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