# Linear Filtering The convolution functions use linear combinations of neighboring pixels to highlight some features in an image. ![](assets/images/easyimage/linear_filter_1.webp)![](assets/images/easyimage/linear_filter_2.webp) * All the convolution functions can be destructive, meaning that the destination image overwrites the source image. * **eVision** offers 2 ways of doing convolutions: * Use a predefined method with a predefined filter, and in some cases, a kernel size. * Create your own [`EKernel`](../../../doxygen_cpp/classeVision_1_1EKernel.html) [.NET](../../../doxygen_cs/classeVision_1_1EKernel.html) [Python](../../../doxygen_python/classeVision_1_1EKernel.html) and use the function [`EasyImage::ConvolKernel`](../../../doxygen_cpp/classeVision_1_1EasyImage.html#a6593a0fdb08e332d6534091c608e115c) [.NET](../../../doxygen_cs/classeVision_1_1EasyImage.html#aee2a47d278abc285097c7c300998a333) [Python](../../../doxygen_python/classeVision_1_1EasyImage.html#a285fdb54dabd903e9bf0a6859e44a347). ## Predefined Filters The available predefined filters are: | * [`EasyImage::ConvolLowpass1`](../../../doxygen_cpp/classeVision_1_1EasyImage.html#a7765c0159a6676c9c9b52cddbea9b5d5) [.NET](../../../doxygen_cs/classeVision_1_1EasyImage.html#a3ae4695b4d48a7c0d839e907922f5002) [Python](../../../doxygen_python/classeVision_1_1EasyImage.html#afeaba983dcb768340492c76e508dd73f)
* [`EasyImage::ConvolLowpass2`](../../../doxygen_cpp/classeVision_1_1EasyImage.html#a89cb0ab1ae7c5dda3a75999a6b655c87) [.NET](../../../doxygen_cs/classeVision_1_1EasyImage.html#a5f100be9bb61458e277c094fe6fb378c) [Python](../../../doxygen_python/classeVision_1_1EasyImage.html#a71caa690c82d6ad2ce6ab44d23594565)
* [`EasyImage::ConvolLowpass3`](../../../doxygen_cpp/classeVision_1_1EasyImage.html#afc99a4eb8ba969b2b1ad6eabb298dbb2) [.NET](../../../doxygen_cs/classeVision_1_1EasyImage.html#a63a0cccd3fc1dcb5428d1ba23d94ab81) [Python](../../../doxygen_python/classeVision_1_1EasyImage.html#af97eca8d7b7bea1d4b0f1c5f9bca39b1)
* [`EasyImage::ConvolHighpass1`](../../../doxygen_cpp/classeVision_1_1EasyImage.html#a5797d794d53579f1965684d3ccd754ef) [.NET](../../../doxygen_cs/classeVision_1_1EasyImage.html#a13f8f1be01a991d808777e238635efba) [Python](../../../doxygen_python/classeVision_1_1EasyImage.html#a4084f33f667d106f7f1390b5eff2f0af)
* [`EasyImage::ConvolHighpass2`](../../../doxygen_cpp/classeVision_1_1EasyImage.html#a13c128a6c3177a7bacf43cfbb695cc64) [.NET](../../../doxygen_cs/classeVision_1_1EasyImage.html#aaad04a76fe725dec26cae5a8764d208c) [Python](../../../doxygen_python/classeVision_1_1EasyImage.html#a7ddb5166fddd18e21fbbaf6916049ced)
* [`EasyImage::ConvolGradient`](../../../doxygen_cpp/classeVision_1_1EasyImage.html#acda51de82a8e63b83043a8548eb2e6a1) [.NET](../../../doxygen_cs/classeVision_1_1EasyImage.html#aa989946fcea5412cf1036255131794d5) [Python](../../../doxygen_python/classeVision_1_1EasyImage.html#a50bd495bd2a6efb7eab1d431f1d07b1e)
* [`EasyImage::ConvolGradientX`](../../../doxygen_cpp/classeVision_1_1EasyImage.html#a148da30b709f387d148f506dc739a350) [.NET](../../../doxygen_cs/classeVision_1_1EasyImage.html#a90e911853f10c872fd0c0ff4186bf314) [Python](../../../doxygen_python/classeVision_1_1EasyImage.html#ad5277d754d4693a2a1e0ccc232096b8b)
* [`EasyImage::ConvolGradientY`](../../../doxygen_cpp/classeVision_1_1EasyImage.html#aadeed40790375c87f40666e8b0ce54c5) [.NET](../../../doxygen_cs/classeVision_1_1EasyImage.html#add90c18a16abafb06cfb75a50c09b0e3) [Python](../../../doxygen_python/classeVision_1_1EasyImage.html#a8140478e308239ce02aef574fa38c32a)
* [`EasyImage::ConvolLaplacian4`](../../../doxygen_cpp/classeVision_1_1EasyImage.html#a840e21cf920ddd43737bd5d289b56258) [.NET](../../../doxygen_cs/classeVision_1_1EasyImage.html#a0f0f0bd20854f3e31fccb1ebffcb5f4b) [Python](../../../doxygen_python/classeVision_1_1EasyImage.html#a5c05b8c783bd30ce9d4e64b0bbf45595)
* [`EasyImage::ConvolLaplacian8`](../../../doxygen_cpp/classeVision_1_1EasyImage.html#a70a0524786918b183a7c0e3c76370e95) [.NET](../../../doxygen_cs/classeVision_1_1EasyImage.html#a44b987f8f302ab68b8c14d2acdb2eb1b) [Python](../../../doxygen_python/classeVision_1_1EasyImage.html#a209a7eb22f9a0372c5e71c762bf0ba16)
* [`EasyImage::ConvolLaplacianX`](../../../doxygen_cpp/classeVision_1_1EasyImage.html#a545c615fea542c70d138bbdeab165af4) [.NET](../../../doxygen_cs/classeVision_1_1EasyImage.html#a80e5606aaa2d07608c534f085f517e77) [Python](../../../doxygen_python/classeVision_1_1EasyImage.html#a9f2362c4e3c7a25aee8cba2d777c53ca)
* [`EasyImage::ConvolLaplacianY`](../../../doxygen_cpp/classeVision_1_1EasyImage.html#aa0e338996d7f32305892994f14559799) [.NET](../../../doxygen_cs/classeVision_1_1EasyImage.html#a91677a819e80ec4b45ee977519b75c01) [Python](../../../doxygen_python/classeVision_1_1EasyImage.html#ad4a7be5a97cf7fb9446099675bfcca5f) | * [`EasyImage::ConvolPrewitt`](../../../doxygen_cpp/classeVision_1_1EasyImage.html#a2aeb4cc8162bb830de26fb6ad006172a) [.NET](../../../doxygen_cs/classeVision_1_1EasyImage.html#ab65e1aafd6e01b5f37f6f4c4738ba364) [Python](../../../doxygen_python/classeVision_1_1EasyImage.html#ad6d9f5eaabbe5f22023f21fe6fab7402)
* [`EasyImage::ConvolPrewittX`](../../../doxygen_cpp/classeVision_1_1EasyImage.html#abfc18cce8cc5370e21347d9f80f41c9a) [.NET](../../../doxygen_cs/classeVision_1_1EasyImage.html#a989d2bc754f6b5633d0bbf6a374a2bcd) [Python](../../../doxygen_python/classeVision_1_1EasyImage.html#a7c334c08a885469b9c3d6ab385eab00a)
* [`EasyImage::ConvolPrewittY`](../../../doxygen_cpp/classeVision_1_1EasyImage.html#a915589fd0708d66db877a2d466abcde4) [.NET](../../../doxygen_cs/classeVision_1_1EasyImage.html#a0d53ad5bfa89f7adb64323f1486ebaa3) [Python](../../../doxygen_python/classeVision_1_1EasyImage.html#acf32db0967c05769bde13dbbb2e73b5c)
* [`EasyImage::ConvolSobel`](../../../doxygen_cpp/classeVision_1_1EasyImage.html#a18a27f58923cfd5c0b42b480f0ab295a) [.NET](../../../doxygen_cs/classeVision_1_1EasyImage.html#af6d3bc69a09f77a46afac5fad5dd60a6) [Python](../../../doxygen_python/classeVision_1_1EasyImage.html#aebb1262c2d49123c96d47cf53d13c315)
* [`EasyImage::ConvolSobelX`](../../../doxygen_cpp/classeVision_1_1EasyImage.html#a5cfd132efdb665bba7f81f60ae6586b2) [.NET](../../../doxygen_cs/classeVision_1_1EasyImage.html#a04eb8dec289036fcaec01047b8832eb6) [Python](../../../doxygen_python/classeVision_1_1EasyImage.html#ace58622dc123d74e66a5e59f91ae7319)
* [`EasyImage::ConvolSobelY`](../../../doxygen_cpp/classeVision_1_1EasyImage.html#a0d362d88c9d13735615d103e247a7d89) [.NET](../../../doxygen_cs/classeVision_1_1EasyImage.html#a90e819be20e871b3f928a1bca5e6c917) [Python](../../../doxygen_python/classeVision_1_1EasyImage.html#a17dd807897cca882a1370311123a92e0)
* [`EasyImage::ConvolRoberts`](../../../doxygen_cpp/classeVision_1_1EasyImage.html#a3cffbd5fe3b682fa8ea6c714d9317565) [.NET](../../../doxygen_cs/classeVision_1_1EasyImage.html#a42c381cf9fa28e9f94f5355eea1a04ad) [Python](../../../doxygen_python/classeVision_1_1EasyImage.html#aa679dfad4ceebe018b3baa2d5b45c75b)
* [`EasyImage::ConvolUniform`](../../../doxygen_cpp/classeVision_1_1EasyImage.html#aaa8ce68270a0bfd82cd6e184d5f2d6ed) [.NET](../../../doxygen_cs/classeVision_1_1EasyImage.html#a95b6b0594caa516696017badeb4c8051) [Python](../../../doxygen_python/classeVision_1_1EasyImage.html#a377cf284f159e96b37692a2096df3afd)
* [`EasyImage::ConvolGaussian`](../../../doxygen_cpp/classeVision_1_1EasyImage.html#a2427f87dc5801fffd97d8ace0571f3aa) [.NET](../../../doxygen_cs/classeVision_1_1EasyImage.html#a38a4452f47c3dce1c31881880a641c39) [Python](../../../doxygen_python/classeVision_1_1EasyImage.html#ae0799598485b88dd6c2eab89ec9c062b)
* [`EasyImage::ConvolGabor`](../../../doxygen_cpp/classeVision_1_1EasyImage.html#a42b3c6e43b7ca4625f4e7da53306afaf) [.NET](../../../doxygen_cs/classeVision_1_1EasyImage.html#a5952df1ea12e591163128579510007fe) [Python](../../../doxygen_python/classeVision_1_1EasyImage.html#a2dd9a94d3104a8505bbaf35ed9ba73b2) | |-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------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## Customized `EKernel` When you use your own kernel: * You can choose the width and height of the kernel. * Use [`EKernel::SetKernelData`](../../../doxygen_cpp/classeVision_1_1EKernel.html#a65ab0e71f70162a5774f56a59155f098) [.NET](../../../doxygen_cs/classeVision_1_1EKernel.html#a8a3bad599fa8bb5e2c91f0a725e88b5d) [Python](../../../doxygen_python/classeVision_1_1EKernel.html#ad9b2d064ab88a86043f92b6e81319dcc) to fill the convolution coefficients. * **eVision** automatically normalizes the kernel coefficients so that their sum is 1. * If you define a [`EKernel::SetGain`](../../../doxygen_cpp/classeVision_1_1EKernel.html#a767a037f41422626cae0905a3bf181ce) [.NET](../../../doxygen_cs/classeVision_1_1EKernel.html#a0e6dc2307eb27ea4a576a1ad69c0d552) [Python](../../../doxygen_python/classeVision_1_1EKernel.html#a5c8325cc5c3d810700fb44b4a63d8184) (multiplying all resulting pixels by that value) and an [`EKernel::SetOffset`](../../../doxygen_cpp/classeVision_1_1EKernel.html#a31798159a4541c193b5827dcf1e116c5) [.NET](../../../doxygen_cs/classeVision_1_1EKernel.html#a738c8b8ddfdd657c6b1d977893178182) [Python](../../../doxygen_python/classeVision_1_1EKernel.html#a3dabceb323e5dc7b057c85d8c3dc4e48) (adding that value to all resulting pixels), they are applied after that normalization. * Note that using any gain other than 1 can lead to a saturation in the resulting image and an overflow or an underflow during the internal calculations. * To rectify any value (to keep only the positive ones for instance), set an [`EKernelRectifier`](../../../doxygen_cpp/namespaceeVision.html#afad3db17c1a46cd1cf56843585ce132d) [.NET](../../../doxygen_cs/namespaceeVision.html#ac35f7d4b783bccb050cf319fa712bf9f) [Python](../../../doxygen_python/classeVision_1_1EKernelRectifier.html). * The outside value is used for the border calculations (when the kernel needs values from out of the image on the border of the image to compute the result). * Another way to create a kernel is by giving the constructor an [`EKernelType`](../../../doxygen_cpp/namespaceeVision.html#a7dc90b4f33628bb10242eecb1887344b) [.NET](../../../doxygen_cs/namespaceeVision.html#a4efdde8ceca649b737f660f01c083e66) [Python](../../../doxygen_python/classeVision_1_1EKernelType.html). This is quite similar to the way of doing a convolution with a predefined filter. ## See Also Easy to understand, ready to copy code snippets. Conceptual guide to choosing and applying spatial and temporal noise reduction. Frequency-selective convolution for texture analysis using the Gabor wavelet.