{"id":398,"date":"2016-08-18T15:19:55","date_gmt":"2016-08-18T14:19:55","guid":{"rendered":"http:\/\/generic.wordpress.soton.ac.uk\/rs4eo\/?page_id=398"},"modified":"2020-09-24T16:26:45","modified_gmt":"2020-09-24T15:26:45","slug":"8-4-high-pass-filters","status":"publish","type":"page","link":"https:\/\/generic.wordpress.soton.ac.uk\/rs4eo\/8-image-processing-part-2\/8-4-high-pass-filters\/","title":{"rendered":"8.4. High Pass Filters"},"content":{"rendered":"<hr \/>\n<p><strong>Objective<\/strong><\/p>\n<p id=\"ta80_23_2\" class=\"block\">The objective of this section is to understand the purpose and operation of high-pass filters in digital image processing.<\/p>\n<hr \/>\n<p>High-pass filters enhance the rapidly varying spatial components within a digital image &#8211; in other words, they enhance the high spatial frequencies. Many high-pass filters are also referred to as <i>edge enhancement filters<\/i>, since they make it easier to detect edges in imagery, such as the transition between two land cover types (land and sea or cropland and forest) or linear features like roads or drainage lines (see Ichoku et al, 1996).<\/p>\n<p><em>N\/B: The slides below will not show on the webpage, but you can save\/keep them on your computer and view them using the Adobe Flash Player 32 you downloaded earlier<\/em><\/p>\n<iframe loading=\"lazy\" src=\"http:\/\/www.edshare.soton.ac.uk\/id\/document\/291617\" width=\"640\" height=\"480\"><\/iframe>\n<p>Perhaps the simplest way to develop a high-pass filter is to run a low-pass pass filter on a digital image, then subtract the output values of the low-pass filter from the input values in the original image. The resultant image has enhanced high spatial frequency information. Another form of high pass filter is known as the <i>first derivative filter<\/i> (see animation). The first derivative filter calculates the gradient of pixels values (i.e. the change in pixel values as you move across an image). Where DN values are constant, the output of a first derivative filter is zero and it only returns non-zero values where DN values change from one pixel to the next. High first derivative values indicate areas of rapid change in the image. The parts of the image that show the greatest change in DN values are known as <i>edges<\/i>. Another high pass filter works by using a kernel in which all the kernel pixel weights are set to -1 except for the central pixel. This central pixel has a weight that corresponds to the total number of pixels in the kernel with weights of -1 (i.e. 8 for a 3 x 3 kernel and 24 for a 5 x 5 kernel). Rather like a low-pass filter, the size of the kernel is directly related to the sharpness of the output image. The illustration below shows the output from a 3&#215;3 high pass filter and a 5&#215;5 filter, together with the kernel weights used to generate the output. Note the greater sharpness in the 5&#215;5 kernel output.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/generic.wordpress.soton.ac.uk\/rs4eo\/wp-content\/uploads\/sites\/106\/2016\/08\/8.16-300x267.jpg\" class=\"alignnone wp-image-399 size-medium\" height=\"267\" width=\"300\" srcset=\"https:\/\/generic.wordpress.soton.ac.uk\/rs4eo\/wp-content\/uploads\/sites\/106\/2016\/08\/8.16-300x267.jpg 300w, https:\/\/generic.wordpress.soton.ac.uk\/rs4eo\/wp-content\/uploads\/sites\/106\/2016\/08\/8.16.jpg 465w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/p>\n<p>Original image<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/generic.wordpress.soton.ac.uk\/rs4eo\/wp-content\/uploads\/sites\/106\/2016\/08\/8.17-264x300.jpg\" class=\"alignnone wp-image-400\" height=\"341\" width=\"300\" srcset=\"https:\/\/generic.wordpress.soton.ac.uk\/rs4eo\/wp-content\/uploads\/sites\/106\/2016\/08\/8.17-264x300.jpg 264w, https:\/\/generic.wordpress.soton.ac.uk\/rs4eo\/wp-content\/uploads\/sites\/106\/2016\/08\/8.17.jpg 464w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/p>\n<p>3&#215;3 high-pass filter<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/generic.wordpress.soton.ac.uk\/rs4eo\/wp-content\/uploads\/sites\/106\/2016\/08\/8.18-240x300.jpg\" class=\"alignnone wp-image-401\" height=\"375\" width=\"300\" srcset=\"https:\/\/generic.wordpress.soton.ac.uk\/rs4eo\/wp-content\/uploads\/sites\/106\/2016\/08\/8.18-240x300.jpg 240w, https:\/\/generic.wordpress.soton.ac.uk\/rs4eo\/wp-content\/uploads\/sites\/106\/2016\/08\/8.18.jpg 461w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/p>\n<p>5&#215;5 high pass filter<\/p>\n<div class=\"FreeTextIdevice\" id=\"id83\">\n<div class=\"iDevice emphasis0\">\n<div id=\"ta83_1\" class=\"block\">\n<p><b>Directional Filters<\/b><\/p>\n<p><i>Directional filters<\/i> are high or low pass filters that enhance a given spatial frequency in their direction of travel. These are helpful in enhancing features with a preferred orientation, such as geological delineations, oceanic waves, roads, urban boundaries, and dune formations. The kernel weights for directional filters can be horizontal (e.g. a 1 row x 3 column kernel, with kernel weights of 1 for each pixel), vertical (e.g. a 3 row x 1 column kernel, again with kernel weights of 1), or diagonal (a 3 row x 3 column kernel, with kernel weights being 1 across the diagonal and zero elsewhere).\u00a0 Examples of the output of horizontal, vertical, and diagonal filters are shown in the example below.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/generic.wordpress.soton.ac.uk\/rs4eo\/wp-content\/uploads\/sites\/106\/2016\/08\/8.19-300x269.jpg\" class=\"alignnone wp-image-402 size-medium\" height=\"269\" width=\"300\" srcset=\"https:\/\/generic.wordpress.soton.ac.uk\/rs4eo\/wp-content\/uploads\/sites\/106\/2016\/08\/8.19-300x269.jpg 300w, https:\/\/generic.wordpress.soton.ac.uk\/rs4eo\/wp-content\/uploads\/sites\/106\/2016\/08\/8.19.jpg 461w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/p>\n<p>Horizontal filter<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/generic.wordpress.soton.ac.uk\/rs4eo\/wp-content\/uploads\/sites\/106\/2016\/08\/8.20-300x269.jpg\" class=\"alignnone wp-image-403 size-medium\" height=\"269\" width=\"300\" srcset=\"https:\/\/generic.wordpress.soton.ac.uk\/rs4eo\/wp-content\/uploads\/sites\/106\/2016\/08\/8.20-300x269.jpg 300w, https:\/\/generic.wordpress.soton.ac.uk\/rs4eo\/wp-content\/uploads\/sites\/106\/2016\/08\/8.20.jpg 461w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/p>\n<p>Vertical filter<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/generic.wordpress.soton.ac.uk\/rs4eo\/wp-content\/uploads\/sites\/106\/2016\/08\/8.21-300x270.jpg\" class=\"alignnone wp-image-404 size-medium\" height=\"270\" width=\"300\" srcset=\"https:\/\/generic.wordpress.soton.ac.uk\/rs4eo\/wp-content\/uploads\/sites\/106\/2016\/08\/8.21-300x270.jpg 300w, https:\/\/generic.wordpress.soton.ac.uk\/rs4eo\/wp-content\/uploads\/sites\/106\/2016\/08\/8.21.jpg 459w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/p>\n<p>Diagonal filter<\/p>\n<hr \/>\n<p><strong>References<\/strong><\/p>\n<div id=\"ta85_24_2\">\n<p><span style=\"font-family: arial,helvetica,sans-serif\">Ichoku A, Meisels A, Chorowicz J (1996) Detection of drainage channel networks on digital satellite images. <i>International Journal of Remote Sensing<\/i><\/span><span style=\"font-family: arial,helvetica,sans-serif\">, 17, 1659\u20131678.<\/span><\/p>\n<hr \/>\n<p><strong>Practical Activity &#8211; Digital Image Enhancement<\/strong><\/p>\n<p id=\"ta86_29_2\" class=\"block\">Download the <a href=\"http:\/\/www.edshare.soton.ac.uk\/id\/document\/293136\" target=\"_blank\" rel=\"noopener noreferrer\">practical instructions and digital image data<\/a>, then undertake the\u00a0 contrast enhancement and spatial filtering operations described in the handout.\u00a0<span>N\/B: The questions in this practical are formative and are not graded(they are meant to enhance your understanding-if you want feedback on them you can post your question\/answers on the discussion board).<\/span><\/p>\n<hr \/>\n<p class=\"block\">\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Objective The objective of this section is to understand the purpose and operation of high-pass filters in digital image processing. High-pass filters enhance the rapidly varying spatial components within a digital image &#8211; in other words, they enhance the high spatial frequencies. Many high-pass filters are also referred to as edge enhancement filters, since they [&hellip;]<\/p>\n","protected":false},"author":1726,"featured_media":0,"parent":365,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-398","page","type-page","status-publish","hentry"],"jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/generic.wordpress.soton.ac.uk\/rs4eo\/wp-json\/wp\/v2\/pages\/398","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/generic.wordpress.soton.ac.uk\/rs4eo\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/generic.wordpress.soton.ac.uk\/rs4eo\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/generic.wordpress.soton.ac.uk\/rs4eo\/wp-json\/wp\/v2\/users\/1726"}],"replies":[{"embeddable":true,"href":"https:\/\/generic.wordpress.soton.ac.uk\/rs4eo\/wp-json\/wp\/v2\/comments?post=398"}],"version-history":[{"count":6,"href":"https:\/\/generic.wordpress.soton.ac.uk\/rs4eo\/wp-json\/wp\/v2\/pages\/398\/revisions"}],"predecessor-version":[{"id":893,"href":"https:\/\/generic.wordpress.soton.ac.uk\/rs4eo\/wp-json\/wp\/v2\/pages\/398\/revisions\/893"}],"up":[{"embeddable":true,"href":"https:\/\/generic.wordpress.soton.ac.uk\/rs4eo\/wp-json\/wp\/v2\/pages\/365"}],"wp:attachment":[{"href":"https:\/\/generic.wordpress.soton.ac.uk\/rs4eo\/wp-json\/wp\/v2\/media?parent=398"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}