![]() Many forms of imaging require some form of illumination. See Why (lossy) JPEGs should not be used in imaging below for details. Do not store raw image data in file formats such as JPEG which use lossy compression. Original data should be saved in a way that preserves the exact sample values. Furthermore, if your objects of interest are described by too few pixels, the error of many statistical computations will be prohibitively high, and some forms of analyses will not be possible at all. Spatial resolution can always be downsampled after the fact-but never upsampled. As a rule of thumb, more samples is better. Digital detectors such as cameras and PMTs can produce sample matrices ranging from 256 x 256 pixels or fewer, up to 128 megapixels or more. Spatial resolution refers to the number (or density, if you prefer) of samples in the image. The goal of this section is to collect information on image acquisition principles that ease the automation of image analysis. Not all data is created equal and thus the analysis of certain images can be easily automated, while others pose a bigger challenge. ![]() Use Help>Update Menus or restart ImageJ to make it appear in the Plugins menu (not necessary if you have used the Fiji Script Editor).The page is a collection of principles for the entire image analysis process, from acquisition to processing to analysis.Again, make sure that you name the file correctly, uppercase/lowercase matters. class file Thresholded_Blur.class into the ImageJ/plugins directory or an immediate subdirectory thereof. Note that “Compile and Run” is currently broken on Fiji as a workaround use File>New>Script, open the Thresholded_Blur.java file and press “Run“. Compile with “Compile and Run” and press “OK”.Make sure that you name the downloaded file ”Thresholded_Blur.java” uppercase/lowercase matters. Copy the raw Thresholded_Blur.java file into the ImageJ plugins folder or a subfolder thereof.Class file Thresholded_Blur.class on GitHub.Source code Thresholded_Blur.java on GitHub (make sure you download the raw file, use the button near the top right).Lee’s Sigma Filter: An edge-preserving filter that does not need manual selection of a threshold.Surface Blur by Douglas Cameron (previously on, currently not moved to, available via the wayback machine): Surface blur with a distance-dependent weight of the pixels.Anyhow, thresholded blur (surface blur) is less prone to staircasing than some other edge-preserving blur filters. On the other hand, if unwanted sharp contours appear in the filtered image (“staircasing” effect), try increasing the softness parameter and use a strength value of 1. The filter can be also used to sharpen edges use strength values > 1 to enhance this effect. “Brightness-based” is advisable for images that have stronger color noise than brightness noise. In both cases, the weights of the colors can be set in Edit>Options>Conversions. Brightness-Based: For RGB images, the difference between two pixels can be calculated as the distance between the points (r,g,b) in a cartesian system or as the difference of brightness (brightness-based).Then, filtering is applied as many times as given by that parameter. Strength: For stronger smoothing, use a value of “Strength” > 1.Typical softness values are between 0 and 2. For strength > 1, the equation uses the softness multiplied by the strength value. In this case, if the difference between the neighbor and the pixel is close to the threshold, i.e., within threshold * (1 - softness) and threshold * (1 + softness), it contributes with a weight between 0 and 1. The threshold should be smaller than the pixel difference across edges that should be preserved, but larger than the noise. ![]() The filter behaves like a usual mean filter if the threshold is larger than the range of the pixels (e.g.
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