{"id":4747,"date":"2025-07-10T15:49:53","date_gmt":"2025-07-10T15:49:53","guid":{"rendered":"https:\/\/www.thermofisher.com\/blog\/life-in-the-lab\/?p=4747"},"modified":"2025-07-10T15:50:01","modified_gmt":"2025-07-10T15:50:01","slug":"deconvolution-101","status":"publish","type":"post","link":"https:\/\/www.thermofisher.com\/blog\/life-in-the-lab\/deconvolution-101\/","title":{"rendered":"Image Deconvolution 101: Guide to Crisp Microscopy"},"content":{"rendered":"\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"800\" height=\"522\" src=\"https:\/\/admin.acceleratingscience.com\/life-in-the-lab\/wp-content\/uploads\/sites\/10\/2025\/07\/Title-Card-Deconvo-1-1.png\" alt=\"Deconvolution 101: Guide to Crisp Microscopy\" class=\"wp-image-4843\" srcset=\"https:\/\/admin.acceleratingscience.com\/life-in-the-lab\/wp-content\/uploads\/sites\/10\/2025\/07\/Title-Card-Deconvo-1-1.png 800w, https:\/\/admin.acceleratingscience.com\/life-in-the-lab\/wp-content\/uploads\/sites\/10\/2025\/07\/Title-Card-Deconvo-1-1-300x196.png 300w, https:\/\/admin.acceleratingscience.com\/life-in-the-lab\/wp-content\/uploads\/sites\/10\/2025\/07\/Title-Card-Deconvo-1-1-768x501.png 768w\" sizes=\"auto, (max-width: 800px) 100vw, 800px\" \/><\/figure>\n\n\n\n<div style=\"height:25px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>Microscopy has significantly advanced our ability to observe the unseen world, but even high-quality images can suffer from blurring due to the physics of light and optical systems. This blurring or background signal can make it challenging to see fine structural details.<\/p>\n\n\n\n<p>Image deconvolution is a computational technique that helps address this issue by reducing background and enhancing image clarity.<\/p>\n\n\n\n<p>In this blog post, we&#8217;ll explore the basics of image deconvolution, how it improves microscopy images, and some tools you can use to apply it to your own data.<\/p>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-16018d1d wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button has-custom-width wp-block-button__width-75 is-style-fill\"><a class=\"wp-block-button__link has-white-color has-text-color has-background has-link-color has-medium-font-size has-text-align-center has-custom-font-size wp-element-button\" href=\"https:\/\/www.thermofisher.com\/us\/en\/home\/life-science\/cell-analysis\/cellular-imaging\/evos-cell-imaging-systems\/models\/evos-m7000.html\" style=\"border-radius:0px;background-color:#ee3134\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>Explore deconvolution with the EVOS M7000 Imaging System<\/strong><\/a><\/div>\n<\/div>\n\n\n\n<div style=\"height:25px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<div class=\"wp-block-yoast-seo-table-of-contents yoast-table-of-contents\"><h2>Table of contents<\/h2><ul><li><a href=\"#h-what-is-deconvolution\" data-level=\"2\">What is deconvolution?<\/a><\/li><li><a href=\"#h-why-is-image-deconvolution-useful-and-what-can-it-do-for-you\" data-level=\"2\">Why is image deconvolution useful and what can it do for you?<\/a><ul><li><a href=\"#h-benefits-of-deconvolution\" data-level=\"3\">Benefits of deconvolution<\/a><\/li><li><a href=\"#h-applications-of-deconvolution-microscopy\" data-level=\"3\">Applications of deconvolution microscopy<\/a><\/li><\/ul><\/li><li><a href=\"#h-how-does-deconvolution-work\" data-level=\"2\">How does deconvolution work?<\/a><ul><li><a href=\"#h-point-spread-function\" data-level=\"3\">Point spread function<\/a><\/li><li><a href=\"#h-deconvolution-algorithms\" data-level=\"3\">Deconvolution algorithms<\/a><\/li><\/ul><\/li><li><a href=\"#h-5-examples-of-2d-image-deconvolution-before-and-after\" data-level=\"2\">5 examples of 2D image deconvolution, before and after<\/a><ul><li><a href=\"#h-1-mouse-kidney\" data-level=\"3\">1. Mouse kidney<\/a><\/li><li><a href=\"#h-2-mouse-colon\" data-level=\"3\">2. Mouse colon \ud83d\udd17<\/a><\/li><li><a href=\"#h-3-hela-cells\" data-level=\"3\">3. HeLa cells<\/a><\/li><li><a href=\"#h-4-bpae-cells\" data-level=\"3\">4. BPAE cells<\/a><\/li><li><a href=\"#h-5-u2os-cells\" data-level=\"3\">5. U2OS cells<\/a><\/li><\/ul><\/li><li><a href=\"#h-more-deconvolution-learning-resources\" data-level=\"2\">More deconvolution learning resources<\/a><\/li><\/ul><\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-what-is-deconvolution\">What is deconvolution? <\/h2>\n\n\n\n<p>When you&#8217;re working with fluorescence microscopy, especially widefield, you&#8217;ve probably noticed that your images can appear a bit blurry or otherwise demonstrate background signal. This blur isn&#8217;t due to a mistake on your part; it&#8217;s a result of out-of-focus light from different focal planes within your sample contributing to the image. Essentially, light from structures above and below your current focal plane gets captured, thus reducing the clarity of the structures you&#8217;re interested in.<\/p>\n\n\n\n<p>Deconvolution is a computational technique that helps address this issue. It uses the point spread function (PSF) of your microscope\u2014a model of how a single point of light behaves in your imaging system\u2014to mathematically reassign out-of-focus light back to its original source. By doing this, deconvolution enhances both the contrast and resolution of your images, making it easier to distinguish fine details in your sample.<\/p>\n\n\n\n<p>There are various algorithms available for image deconvolution, ranging from simple deblurring methods to more complex iterative approaches. Some advanced techniques even incorporate deep learning to improve the accuracy of the PSF estimation and the deconvolution process itself.<\/p>\n\n\n\n<p>In practice, applying deconvolution to your microscopy images can significantly improve their quality, making it a valuable tool for anyone looking to get the most out of their fluorescence microscopy data.<\/p>\n\n\n\n<div style=\"height:15px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<figure data-wp-context=\"{&quot;imageId&quot;:&quot;6aa70f8048659&quot;}\" data-wp-interactive=\"core\/image\" data-wp-key=\"6aa70f8048659\" class=\"wp-block-image aligncenter size-full wp-lightbox-container\"><img loading=\"lazy\" decoding=\"async\" width=\"804\" height=\"451\" data-wp-class--hide=\"state.isContentHidden\" data-wp-class--show=\"state.isContentVisible\" data-wp-init=\"callbacks.setButtonStyles\" data-wp-on--click=\"actions.showLightbox\" data-wp-on--load=\"callbacks.setButtonStyles\" data-wp-on-window--resize=\"callbacks.setButtonStyles\" src=\"https:\/\/admin.acceleratingscience.com\/life-in-the-lab\/wp-content\/uploads\/sites\/10\/2025\/07\/D1-Image.png\" alt=\"Deconvolution of a widefield HeLa cell image with fluorescent staining shows a much clearer image of cellular structure\" class=\"wp-image-4813\" srcset=\"https:\/\/admin.acceleratingscience.com\/life-in-the-lab\/wp-content\/uploads\/sites\/10\/2025\/07\/D1-Image.png 804w, https:\/\/admin.acceleratingscience.com\/life-in-the-lab\/wp-content\/uploads\/sites\/10\/2025\/07\/D1-Image-300x168.png 300w, https:\/\/admin.acceleratingscience.com\/life-in-the-lab\/wp-content\/uploads\/sites\/10\/2025\/07\/D1-Image-768x431.png 768w\" sizes=\"auto, (max-width: 804px) 100vw, 804px\" \/><button\n\t\t\tclass=\"lightbox-trigger\"\n\t\t\ttype=\"button\"\n\t\t\taria-haspopup=\"dialog\"\n\t\t\taria-label=\"Enlarge\"\n\t\t\tdata-wp-init=\"callbacks.initTriggerButton\"\n\t\t\tdata-wp-on--click=\"actions.showLightbox\"\n\t\t\tdata-wp-style--right=\"state.imageButtonRight\"\n\t\t\tdata-wp-style--top=\"state.imageButtonTop\"\n\t\t>\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"12\" height=\"12\" fill=\"none\" viewBox=\"0 0 12 12\">\n\t\t\t\t<path fill=\"#fff\" d=\"M2 0a2 2 0 0 0-2 2v2h1.5V2a.5.5 0 0 1 .5-.5h2V0H2Zm2 10.5H2a.5.5 0 0 1-.5-.5V8H0v2a2 2 0 0 0 2 2h2v-1.5ZM8 12v-1.5h2a.5.5 0 0 0 .5-.5V8H12v2a2 2 0 0 1-2 2H8Zm2-12a2 2 0 0 1 2 2v2h-1.5V2a.5.5 0 0 0-.5-.5H8V0h2Z\" \/>\n\t\t\t<\/svg>\n\t\t<\/button><figcaption class=\"wp-element-caption\"><em>HeLa cell widefield vs deconvolved. EVOS M7000, 60x objective, Celleste Image Analysis Software. Stains: NucBlue (nucleus), Phalloidin (F-actin).<\/em><\/figcaption><\/figure>\n\n\n\n<div style=\"height:15px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-why-is-image-deconvolution-useful-and-what-can-it-do-for-you\">Why is image deconvolution useful and what can it do for you? <\/h2>\n\n\n\n<p>Image deconvolution is a powerful technique that enhances image clarity. Here&#8217;s why it&#8217;s useful and how you can leverage it in your research.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-benefits-of-deconvolution\">Benefits of deconvolution<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Improved Image Clarity<\/strong>: Deconvolution algorithms reassign out-of-focus light back to its source, resulting in sharper images with enhanced contrast and resolution.<\/li>\n\n\n\n<li><strong>Enhanced Signal-to-Noise Ratio (SNR)<\/strong>: By reducing background noise, deconvolution improves the SNR, making it easier to detect and analyze faint signals.<\/li>\n\n\n\n<li><strong>Better 3D Visualization<\/strong>: When applied to Z-stacks, deconvolution refines the axial resolution, offering more accurate three-dimensional reconstructions of your samples.<\/li>\n\n\n\n<li><strong>Quantitative Accuracy<\/strong>: Sharper images lead to more precise measurements of structures, intensities, and spatial relationships within your samples.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-applications-of-deconvolution-microscopy\">Applications of deconvolution microscopy<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Live-Cell Imaging<\/strong>: Enhance <a href=\"https:\/\/www.thermofisher.com\/us\/en\/home\/life-science\/cell-analysis\/cellular-imaging\/evos-cell-imaging-systems\/models\/evos-m7000.html?ef_id=Cj0KCQjwlYHBBhD9ARIsALRu09rF31bmEgaGzZDvzK0OQTOkySXPh-CMw9FC-FNh6tkZ-hkJWZ80jbMaAmtlEALw_wcB:G:s&amp;s_kwcid=AL!3652!3!586406504876!e!!g!!thermo%20fisher%20evos%20m7000!9768035657!98974177639&amp;cid=bid_pca_iie_r01_co_cp1359_pjt0000_bid00000_0se_gaw_bt_lgn_ins&amp;gad_source=1&amp;gad_campaignid=9768035657&amp;gbraid=0AAAAADxi_GSw2-gt0I0edtgM0eeBwqgoz&amp;gclid=Cj0KCQjwlYHBBhD9ARIsALRu09rF31bmEgaGzZDvzK0OQTOkySXPh-CMw9FC-FNh6tkZ-hkJWZ80jbMaAmtlEALw_wcB#media\">time-lapse sequences<\/a> by reducing blur, allowing for clearer observation of dynamic processes. Minimize fluorescence exposure, increasing the viability of live cells.<\/li>\n\n\n\n<li><strong>Thick Tissue Imaging<\/strong>: Improve clarity in thick specimens where out-of-focus light is prevalent, thereby aiding in the study of complex tissues.<\/li>\n\n\n\n<li><strong>Co-Localization Studies<\/strong>: Achieve more accurate overlap analysis of multiple fluorescent markers by minimizing signal bleed-through.<\/li>\n\n\n\n<li><strong>Super-Resolution Techniques<\/strong>: Combine deconvolution with methods like structured illumination microscopy (SIM) to push beyond traditional resolution limits.<\/li>\n<\/ul>\n\n\n\n<div style=\"height:25px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-how-does-deconvolution-work\">How does deconvolution work? <\/h2>\n\n\n\n<p>Deconvolution relies on complex image algorithms, and ease of use often depends on the tools used to implement them.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-point-spread-function\">Point spread function<\/h3>\n\n\n\n<p>In microscopy, the<strong> point spread function (PSF) <\/strong>describes how a point source of light\u2014like a fluorescent molecule\u2014appears in an image due to the diffraction and imperfections inherent in the optical system. Instead of a perfect point, the light spreads out, creating a characteristic pattern that reflects how a specific microscope blurs the image.<\/p>\n\n\n\n<p>By modeling the PSF, deconvolution algorithms can reassign out-of-focus light back to its origin, enhancing image clarity and resolution. This process is particularly beneficial in fluorescence microscopy, where out-of-focus light can significantly obscure fine structural details.<\/p>\n\n\n\n<p>The PSF can be determined theoretically, based on the microscope&#8217;s optical parameters, or empirically, by imaging sub-resolution beads and analyzing how their light spreads. Accurate knowledge of the PSF allows for more effective deconvolution, leading to sharper, more detailed images that better represent the actual structure of the specimen.<\/p>\n\n\n\n<p>The EVOS Analysis deconvolution tool uses an adaptive PSF, a type of constrained-iterative computational algorithm. Unlike methods that use digital haze reduction, an adaptive PSF restores images by reassigning scattered light to its original location, reducing background fluorescence and sharpening the fluorescence signal. This technique can resolve faint, blurred details in the original image. With appropriate controls, an image deconvolved via an adaptive PSF may be used for quantitative fluorescence measurements.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-deconvolution-algorithms\">Deconvolution algorithms<\/h3>\n\n\n\n<p>In microscopy, deconvolution algorithms are essential for enhancing image clarity by mitigating blurring effects introduced by the optical system&#8217;s point spread function (PSF). These algorithms primarily fall into two categories: inverse filter methods and iterative methods.<\/p>\n\n\n\n<p><strong>Iterative algorithms<\/strong> approach deconvolution as a progressive refinement process. They start with an initial estimate of the true image and repeatedly update this estimate to minimize the difference between the observed image and the convolution of the estimate with the PSF. This approach is particularly effective in handling noise and complex blurring.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Richardson\u2013Lucy (RL) Deconvolution:<\/strong> Assumes Poisson noise, commonly found in photon-limited imaging. It progressively refines the image estimate, providing high-quality restorations, especially in low-light conditions.<\/li>\n\n\n\n<li><strong>Maximum Likelihood Estimation (MLE):<\/strong> A statistical approach that estimates the most probable original image given the observed data and noise characteristics. It&#8217;s robust but computationally intensive, often used in high-precision applications.<\/li>\n\n\n\n<li><strong>Landweber Iteration:<\/strong> A simple iterative method that updates the image estimate by moving in the direction opposite to the gradient of the error. While straightforward, it can be slow to converge and may require many iterations.<\/li>\n\n\n\n<li><strong>Total Variation (TV) Regularization:<\/strong> Incorporates a regularization term to preserve edges while reducing noise. It&#8217;s beneficial for images with sharp features, maintaining structural integrity during deconvolution.<\/li>\n<\/ul>\n\n\n\n<p><strong>Inverse filter methods<\/strong> aim to directly reverse the blurring effect by applying an inverse operation to the PSF. These methods are generally faster, but can be more sensitive to noise.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Na\u00efve Inverse Filtering:<\/strong> Directly divides the Fourier transform of the observed image by the Fourier transform of the PSF. While simple, it&#8217;s highly sensitive to noise.<\/li>\n\n\n\n<li><strong>Wiener Deconvolution:<\/strong> An extension of inverse filtering that incorporates a noise-to-signal ratio, aiming to minimize the mean square error between the estimated and true images. It allows a balance between deblurring and noise suppression.<\/li>\n\n\n\n<li><strong>Tikhonov Regularization:<\/strong> Adds a regularization term to stabilize the inversion process, reducing the impact of noise and improving the robustness of the deconvolution.<\/li>\n\n\n\n<li><strong>Blind deconvolution: <\/strong>Used when the PSF is unknown, this method simultaneously estimates the PSF and the deblurred image. It&#8217;s particularly useful when measuring the PSF is impractical, though it requires careful implementation to avoid artifacts.<\/li>\n\n\n\n<li><strong>AI-powered algorithms<\/strong>: Recent advancements involve using deep learning models, such as convolutional neural networks (CNNs), to perform deconvolution. These models can learn complex mappings from blurred to sharp images, offering improved performance in certain scenarios.<\/li>\n<\/ul>\n\n\n\n<div style=\"height:25px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-5-examples-of-2d-image-deconvolution-before-and-after\">5 examples of 2D image deconvolution, before and after<\/h2>\n\n\n\n<p>These examples showcase the difference that deconvolution can make for analysis and publication purposes. <\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-1-mouse-kidney\">1. Mouse kidney<\/h3>\n\n\n\n<figure class=\"wp-block-jetpack-image-compare\"><div class=\"juxtapose\" data-mode=\"vertical\"><img loading=\"lazy\" decoding=\"async\" id=\"4831\" src=\"https:\/\/admin.acceleratingscience.com\/life-in-the-lab\/wp-content\/uploads\/sites\/10\/2025\/07\/1a-2.jpg\" alt=\"\" width=\"500\" height=\"500\" class=\"image-compare__image-before\" \/><img loading=\"lazy\" decoding=\"async\" id=\"4832\" src=\"https:\/\/admin.acceleratingscience.com\/life-in-the-lab\/wp-content\/uploads\/sites\/10\/2025\/07\/1b-1-1.jpg\" alt=\"\" width=\"500\" height=\"500\" class=\"image-compare__image-after\" \/><\/div><figcaption>FluoCells\u2122 Prepared Slide #3 (mouse kidney section with Alexa Fluor\u2122 488 WGA, Alexa Fluor\u2122 568 Phalloidin, and DAPI), imaged at 4X using EVOS M7000 Imaging System.<\/figcaption><\/figure>\n\n\n\n<div style=\"height:25px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-2-mouse-colon\">2. Mouse colon<div class=\"glfh_linkContainer\"><a title=\"Copy link to clipboard\" href=\"https:\/\/admin.acceleratingscience.com\/life-in-the-lab\/wp-admin\/post.php?post=4747&amp;action=edit#block-fe0d891f-e10f-43f6-97cd-7803f86baf73\">\ud83d\udd17<\/a><\/div><\/h3>\n\n\n\n<figure class=\"wp-block-jetpack-image-compare\"><div class=\"juxtapose\" data-mode=\"vertical\"><img loading=\"lazy\" decoding=\"async\" id=\"4833\" src=\"https:\/\/admin.acceleratingscience.com\/life-in-the-lab\/wp-content\/uploads\/sites\/10\/2025\/07\/2a-1.jpg\" alt=\"\" width=\"500\" height=\"500\" class=\"image-compare__image-before\" \/><img loading=\"lazy\" decoding=\"async\" id=\"4834\" src=\"https:\/\/admin.acceleratingscience.com\/life-in-the-lab\/wp-content\/uploads\/sites\/10\/2025\/07\/2b-1.jpg\" alt=\"\" width=\"500\" height=\"500\" class=\"image-compare__image-after\" \/><\/div><figcaption>Mouse colon labeled with Alexa Fluor\u2122 488 conjugated CD4 primary antibody and DAPI, mounted in ProLong antifade mountant, and imaged on EVOS M7000 Imaging System at 60X magnification.\u00a0<\/figcaption><\/figure>\n\n\n\n<div style=\"height:25px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-3-hela-cells\">3. HeLa cells <\/h3>\n\n\n\n<figure class=\"wp-block-jetpack-image-compare\"><div class=\"juxtapose\" data-mode=\"vertical\"><img loading=\"lazy\" decoding=\"async\" id=\"4835\" src=\"https:\/\/admin.acceleratingscience.com\/life-in-the-lab\/wp-content\/uploads\/sites\/10\/2025\/07\/3a-1.jpg\" alt=\"\" width=\"500\" height=\"500\" class=\"image-compare__image-before\" \/><img loading=\"lazy\" decoding=\"async\" id=\"4836\" src=\"https:\/\/admin.acceleratingscience.com\/life-in-the-lab\/wp-content\/uploads\/sites\/10\/2025\/07\/3b.jpg\" alt=\"\" width=\"500\" height=\"500\" class=\"image-compare__image-after\" \/><\/div><figcaption>Hela cells (PFA fixed) labeled with DAPI, anti-Coilin Antibody, Beta-Tubulin Monoclonal Antibody, and Ki-67 Recombinant Rat Monoclonal Antibody. Imaged on EVOS M7000 Imaging System at 20X magnification using ProLong mounting media.\u00a0<\/figcaption><\/figure>\n\n\n\n<div style=\"height:25px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-4-bpae-cells\">4. BPAE cells<\/h3>\n\n\n\n<figure class=\"wp-block-jetpack-image-compare\"><div class=\"juxtapose\" data-mode=\"vertical\"><img loading=\"lazy\" decoding=\"async\" id=\"4837\" src=\"https:\/\/admin.acceleratingscience.com\/life-in-the-lab\/wp-content\/uploads\/sites\/10\/2025\/07\/4a.png\" alt=\"\" width=\"500\" height=\"374\" class=\"image-compare__image-before\" \/><img loading=\"lazy\" decoding=\"async\" id=\"4838\" src=\"https:\/\/admin.acceleratingscience.com\/life-in-the-lab\/wp-content\/uploads\/sites\/10\/2025\/07\/4b.jpg\" alt=\"\" width=\"500\" height=\"374\" class=\"image-compare__image-after\" \/><\/div><figcaption>FluoCells\u2122 Prepared Slide #1 (BPAE cells with MitoTracker\u2122 Red CMXRos, Alexa Fluor\u2122 488 Phalloidin, and DAPI), imaged on EVOS M7000 Imaging System at 40X magnification.<\/figcaption><\/figure>\n\n\n\n<div style=\"height:25px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-5-u2os-cells\">5. U2OS cells<\/h3>\n\n\n\n<figure class=\"wp-block-jetpack-image-compare\"><div class=\"juxtapose\" data-mode=\"vertical\"><img loading=\"lazy\" decoding=\"async\" id=\"4839\" src=\"https:\/\/admin.acceleratingscience.com\/life-in-the-lab\/wp-content\/uploads\/sites\/10\/2025\/07\/6a.jpg\" alt=\"\" width=\"500\" height=\"375\" class=\"image-compare__image-before\" \/><img loading=\"lazy\" decoding=\"async\" id=\"4840\" src=\"https:\/\/admin.acceleratingscience.com\/life-in-the-lab\/wp-content\/uploads\/sites\/10\/2025\/07\/6b.jpg\" alt=\"\" width=\"500\" height=\"376\" class=\"image-compare__image-after\" \/><\/div><figcaption>U2OS cells plated in Nunc\u2122 Glass Bottom Dishes (Cat. No. 150682) and labeled with Hoechst 33342, trihydrochloride trihydrate, 10 mg\/mL (Cat. No. H3570, nucleus-blue), Alexa Fluor\u2122 488 phalloidin (Cat. No. A12379, actin-green), and alpha Tubulin Monoclonal Antibody (236-10501) (Cat. No. A11126) with Goat anti-Mouse IgG (H+L) Cross-Adsorbed Secondary Antibody, Alexa Fluor\u2122 647 (Cat. No. A21235, tubulin-purple). Imaged on EVOS M7000 Imaging System (Cat. No. AMF7000) with EVOS 100X Oil Objective, fluorite, coverslip-corrected (Cat. No. AMEP4696).\u00a0<\/figcaption><\/figure>\n\n\n\n<div style=\"height:25px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-more-deconvolution-learning-resources\">More deconvolution learning resources<\/h2>\n\n\n\n<p>You can learn more about imaging, deconvolution, and more at thermofisher.com:<strong><\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/www.thermofisher.com\/us\/en\/home\/life-science\/cell-analysis\/cellular-imaging\/evos-cell-imaging-systems\/sample-data.html#video-gallery\" target=\"_blank\" rel=\"noreferrer noopener\">EVOS image gallery and sample data<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/www.thermofisher.com\/us\/en\/home\/life-science\/cell-analysis\/cellular-imaging\/evos-cell-imaging-systems\/sample-data.html#sample-applications\" target=\"_blank\" rel=\"noreferrer noopener\">Imaging app note library<\/a> \u2013 wound healing, cell viability, phagocytosis, CRISPR knockout, spheroid analysis, and more<\/li>\n\n\n\n<li><a href=\"https:\/\/www.thermofisher.com\/order\/stain-it\/#!\/\" target=\"_blank\" rel=\"noreferrer noopener\">Stain-iT Cell Staining Simulator<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/www.thermofisher.com\/us\/en\/home\/life-science\/cell-analysis\/cellular-imaging\/cell-imaging-systems\/evos-citations.html\" target=\"_blank\" rel=\"noreferrer noopener\">EVOS Cell Imaging Systems citations<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/www.thermofisher.com\/us\/en\/home\/life-science\/cell-analysis\/cellular-imaging\/evos-cell-imaging-systems\/resources.html#documents-and-downloads\" target=\"_blank\" rel=\"noreferrer noopener\">Customer stories: using EVOS imaging systems<\/a><\/li>\n<\/ul>\n\n\n\n<div style=\"height:15px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-16018d1d wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button has-custom-width wp-block-button__width-75 is-style-fill\"><a class=\"wp-block-button__link has-white-color has-text-color has-background has-link-color has-medium-font-size has-text-align-center has-custom-font-size wp-element-button\" href=\"https:\/\/www.thermofisher.com\/us\/en\/home\/life-science\/cell-analysis\/cellular-imaging\/evos-cell-imaging-systems\/models\/evos-m7000.html\" style=\"border-radius:0px;background-color:#ee3134\" target=\"_blank\" rel=\"noreferrer noopener\"><strong><strong>Explore deconvolution with the EVOS M7000 Imaging System<\/strong><\/strong><\/a><\/div>\n<\/div>\n\n\n\n<p>##<\/p>\n\n\n\n<p><em>\u00a9 2025 Thermo Fisher Scientific Inc. All rights reserved. All trademarks are the property of Thermo Fisher Scientific and its subsidiaries unless otherwise<\/em>&nbsp;<em>specified.<\/em><\/p>\n\n\n\n<p><em>For Research Use Only. Not for use in diagnostic procedures.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Microscopy has significantly advanced our ability to observe the unseen world, but even high-quality images can suffer from blurring due to the physics of light and optical systems. This blurring or background signal can make it challenging to see fine structural details. Image deconvolution is a computational technique that helps address this issue by reducing<\/p>\n","protected":false},"author":1670,"featured_media":4843,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_kad_blocks_custom_css":"","_kad_blocks_head_custom_js":"","_kad_blocks_body_custom_js":"","_kad_blocks_footer_custom_js":"","_monsterinsights_skip_tracking":false,"_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[976,4],"tags":[],"division":[],"class_list":{"0":"post-4747","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-connect-to-science","8":"category-general","9":"entry"},"_selected_authors":[],"_selected_reviewers":[],"acf":[],"yoast_head":"<!-- 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