While sites like Facebook and Instagram have their own built-in filters and photo effects available, the filters they offer are far from the best. That is because online editors can not only offer many more varieties; they can also offer more in terms of customization and fine-tuning. Online selfie filters like Fotor.com has all the tools you need to make your image go from bland to share-worthy in a matter of moments! Let's take a look at just some of the popular online photo filters that Fotor has to offer!
VSCO Filter
To create a VSCO inspired filter effect you will want to use a vibrant preset. VSCO-like filters are great for a variety of images. Giving your images a colorful, vibrant look that works well on subjects like nature and beach scenes.
Filter: Spring – Renewed (Before vs. After)
Follow me on Facebook-me on Instagram-for watching this video 🤗, i hop you like. Disimpan oleh Fiat Rattikar. Orang-orang juga menyukai ide ini Pinterest. PicsArt Filter: MV3. PicsArt is the #1 photo & video editing app and creative community. Awesome starts here! Sweet Magic: 60+ Fotos Tumblr no Supermercado para se.
Vintage Photo Filter
Fotor has a number of photo filter effects found under the 'Vintage' section that is aimed at giving your photos and old-timey feel. This 'Adler' filter fades out the darker areas while giving everything a nice vintage yellow tone!
Filter: Vintage – Alder (Before vs. After)
Oil Painting Filter
Tired of having to open up Photoshop to add an oil painting filter to your photos? Add one in just a matter of a few clicks under 'Oil Painted'! The 'Sheened' gives everything a wash of green, extra perfect for leavy green photos.
Free Online Photo Editor. Photo, sketch and paint effects. For Tumblr, Facebook, Chromebook or WebSites. Lunapics Image software free image, art & animated Gif creator. A simple filter for your picture can make a significant difference in your final result. PicsArt's Light Effects tool offers a wide range of filters and photo effects that will completely transform your images. From soft grunge aesthetic all the way to more vivid and bold color filters, browse through them all and apply them to your photos!
Filter: Oil Painted – Sheened (Before vs. After)
90s Photo Filter
Do you miss the 90's as much as I do? Bring them back using any of the filters found in the 'Cool' category. Ranging from bright vivid filters to warm-toned filters like 'Early Bird' seen below.
Filter: Cool – Early Bird (Before vs. After)
Retro Filter
If you are after a more of a retro '50s and '60s vibe then check out the 'Retro' category to get those faded vibes. Inspired by the fashion magazines of the retro era, that still works with modern Instagram photos today!
Filter: Retro – Warm Green (Before vs. After)
Fade/Vignette
Vignettes help bring focus to a subject by darkening the corners of an image. And the best thing about creating a vignette in Photo is that you can control how intense the vignette is by just using a simple slider.
Filter: Vignette (Before vs. After)
Art Filter Photo
Fotor has a number of different art filters that will not only color but also brighten and vignette your imageto get them Instagram ready. The 'Graphik' filters put a contemporary twist on photos, perfect for fashion images!
Filter: Artistic – Graphik (Before vs. After)
Grayscale Filter
Turn your photos black and white in a matter of seconds with Fotors 'Greyscale', 'Chromed' and 'Black&White' options! The 'Faded White' filter also added a subtle distressed effect.
Filter: Greyscale – Faded White (Before vs. After)
Pastel Filter
Now available here. Use filter 'Notre Dame' found under the 'Groovy' section to achieve a quick and easy pastel filter effect which will give a whimsical fantasy vibe to any photo. There is a multitude of other pastel filters ranging from greens to blues and purples!
Filter: Groovy – Notre Dame (Before vs. After)
Christmas Filter
Add festive frames in a pinch with the 'Christmas' frames option. These frames are not only perfect for sharing on social media, but also for creating holiday cards that you can then print out and send to your family and friends!
Filter: Christmas Frame (Before vs. After)
Polaroid
Change your digital photo into a Polaroid with this polaroid frame! The same Polaroid effect, without having to buy an expensive camera and film! And best of all, it's much easier to share online. Control print screen.
Filter: Polaroid Frame (Before vs. After)
Blur Photo
Add a tilt-shift blur to a photo to create more depth and interest to a photo. You can control where the blur is and how much blur you want as it is a selective blur app! You can blur out things in the foreground and the background, creating both a bokeh and depth of field effect.
Filter: Tilt-Shift
Nature Photo Effect
Bring out the deep greens of a forest, or the bright blues of the ocean with the 'Sunned Up' filter pack, perfect for outdoor photos of lush trees and wilderness. You can layer on the effect as many times as you want to get an even more intense filter effect.
Filter: Sunned Up – S4 (Before vs. After)
Sparkle
Add a number of different sparkle effects using the 'Festive' filters. This includes a 'Starlight' inspired filter that will give every image a tiny touch of magic. You can also use these filters to create a snowy effect!
Filter: Festive – Starlight (Before vs. After)
Bokeh Effect
Add an easy bokeh effect without having to open Photoshop. Want something more interesting than just circles? Try this 'Hearts' filter for a bloom of colorful hearts instead that will also give your photo a slight vintage vibe!
Filter: Festive – Hearts (Before vs. After)
Pixelate Image
Pixelate either the whole image or just parts using the Pixelate filter. Perfect for a trendy glitch effect or to censor out words and faces! Don't worry about messing up, as you can always erase the pixel effect if you end up pixelating too much!
Filter: Pixelate (Before vs. After)
Light Leak
Add a light leak to your photo to add even more of a vintage or retro vibe. These light leaks pair perfectly with both the vintage and retro filters, and can be mixed and matched with any other filter found on Fotor!
Filter: Sun Spot (Before vs. After)
Lens Flare
Add one of many different lens flares to your photo, with the best part being you get to move and control where the lens flare is placed! You also get to control the intensity by simply adjusting a couple of sliders!
Filter: Lens Flare – Sun Kissed (Before vs. After)
Lomo Effect
Get aLomo effect with a number of lo-fi inspired filters! Like all other Fotor photo filters and effects, you get to choose the intensity. You can also layer on some light leaks to bring even more light into an image, so don't be afraid to mix and match!
Filter: LOMO – Sangria (Before vs. After)
How to Add Filters
Now that you have some inspiration, let's take a look at how to add a popular online photo filter, border or pixelate an image in just a few quick and easy steps!
Once you have all of your filters and edits applied, you will be all set to share them on your social media. Let's get started!
Step 1
Choose 'Edit Photo' on Fotor.com.
Step 2
Upload the photo you want to edit by going to 'Open' and choosing where your file is located. You can also open more than one photo at a time if needed!
Step 3
On the left-hand side choose 'Effect.' Here you will find dozens of photo filters to choose from! Choose the one that best suits you and your photo!
Control the intensity of the photo using the slider directly below the filter effect preview and name. Set the filter to 100% to get an intense filter effect.
Press 'Apply' once happy. Mix and match as many photo filters as you want! Just remember to clip 'Apply' with ever filter.
Step 4
Add a frame by going to 'Frames' found on the left-hand side.
Choose your frame, and then hit 'Apply!' just as you would with the filters.
Step 5
Go back to the 'Effects' area and scroll to the bottom of the filters to find 'Pilxelate'
Choose the size of your brush, and the Pixel Size using the sliders below the filter name. You can change these as you go!
Use the mouse to paint a pixel effect anywhere you click and drag on the photo. Once happy with the effect hit 'Apply.'
Step 6
Save your photo by clicking 'Save' found in the right-hand corner, setting the Quality to 'High' and then you are ready to download and post!
F.A.Q
Which popular online photo filter is best for selfies?
For your normal, everyday filter you want something that increases the vibrancy as well as the contrast and lighting to really make you 'Pop!'. Look in the 'Cool' and 'Sunned Up' sections for the best selfie filters.
How do you get old photo filters?
Look at the 'Vintage', 'Retro' and 'Grayscale' filters to make your photos look old and retro. This will also include grunge and distress effects.
How do you change the color of a photo?
In the Basic tool options, you can adjust both the Tint and Temperature of a photo by looking under 'Color' and adjusting the sliders.
What is the best free alternative to Photoshop?
Fototr gives you all the tools you need to edit a photo, including adding graphics, pixelation, bokeh and tilt-shift blur without having to download or learn a new program!
Outro
There you have it! 19 cool and popular photo filters online that can all be done in just a matter of moments right in your browser. No heavy photoshop or other editing software needed! And it's perfect for everyone, even if you've never edited a photo before!
Filters not only help your photos stand out from the crowd, but they also help bring out your creative side. Allowing you to turn an everyday average photo into something much more special! So, go out there and express yourself and how the world your unique style.
Mavis beacon teaches typing 1995. About Us:
Fotor is a free online picture editor and graphic designer, allowing you to use online photo editing tools, such as add filters, frames, text, stickers, and effects…and apply design tools to make creative photo designs and graphics. Online photoshop and graphic design software have never been so easy!
Table of Contents
Back to indexIntroduction
Image filtering allows you to apply various effects on photos. Thetype of image filtering described here uses a 2D filter similar tothe one included in Paint Shop Pro as User Defined Filter and inPhotoshop as Custom Filter.Convolution
The trick of image filtering is that you have a 2D filter matrix,and the 2D image. Then, for every pixel of the image, take the sumof products. Each product is the color value of the current pixelor a neighbor of it, with the corresponding value of the filtermatrix. The center of the filter matrix has to be multiplied withthe current pixel, the other elements of the filter matrix withcorresponding neighbor pixels.This operation where you take the sum of products of elements fromtwo 2D functions, where you let one of the two functions move overevery element of the other function, is called Convolution orCorrelation. The difference between Convolution and Correlation isthat for Convolution you have to mirror the filter matrix, butusually it's symmetrical anyway so there's no difference.
The filters with convolution are relatively simple. More complexfilters, that can use more fancy functions, exist as well, and cando much more complex things (for example the Colored Pencil filterin Photoshop), but such filters aren't discussed here.
The 2D convolution operation requires a 4-double loop, so it isn'textremely fast, unless you use small filters. Here we'll usually beusing 3x3 or 5x5 filters.
There are a few rules about the filter:
- Its size has to be uneven, so that it has a center, forexample 3x3, 5x5 and 7x7 are ok.
- It doesn't have to, but the sum of all elements of the filtershould be 1 if you want the resulting image to have the samebrightness as the original.
- If the sum of the elements is larger than 1, the result will bea brighter image, and if it's smaller than 1, a darker image. Ifthe sum is 0, the resulting image isn't necessarily completelyblack, but it'll be very dark.
The resulting pixel values after applying the filter can benegative or larger than 255, if that happens you can truncate themso that values smaller than 0 are made 0 and values larger than 255are set to 255. For negative values, you can also take the absolutevalue instead.
In the Fourier Domain or Frequency Domain, the convolutionoperation becomes a multiplication instead, which is faster. In theFourier Domain, much more powerful and bigger filters can beapplied faster, especially if you use the Fast Fourier Transform.More about this is in the Fourier Transform article. In thisarticle, we'll look at a few very typical small filters, such asblur, edge detection and emboss.
Image filters aren't feasible for real time applications and gamesyet, but they're useful in image processing.
Digital audio and electronic filters work with convolution as well,but in 1D.
Mv3 Filter Picsart Background
Here's the code that'll be used to try out different filters. Apartfrom using a filter matrix, it also has a multiplier factor and abias. After applying the filter, the factor will be multiplied withthe result, and the bias added to it. So if you have a filter withan element 0.25 in it, but the factor is set to 2, all elements ofthe filter are in theory multiplied by two so that element 0.25 isactually 0.5. The bias can be used if you want to make theresulting image brighter.
The result of one pixel is stored in floats red, green and blue,before converting it to the integer value in the result buffer.
The filter calculation itself is a 4-double loop that has to gothrough every pixel of the image, and then through every element ofthe filter matrix. The location imageX and imageY is calculated sothat for the center element of the filter it'll be x, y, but forthe other elements it'll be a pixel from the image to the left,right, top or bottom of x, y. Its modulo divided through the width(w) or height (h) of the image so that pixels outside the imagewill be wrapped around. Before modulo dividing it, w or h are alsoadded to it, because this modulo division doesn't work correctlyfor negative values. Now, pixel (-1, -1) will correctly becomepixel (w-1, h-1).
If you want to take the absolute value of values smaller than zeroinstead of truncating it, use this code instead:
The filter filled in currently,
[ 0 0 0 ]
[ 0 1 0 ]
[ 0 0 0 ],
does nothing more than returning the original image, since only thecenter value is 1 so every pixel is multiplied with 1.
The code tries to load the image 'pics/photo3.bmp'. This image canbe downloaded here.
The original image looks like this:
Now we'll apply several filters to the image by changing thedefinition of the filter array and running the code.
Blur
Blurring is done for example by taking the average of the currentpixel and its 4 neighbors. Take the sum of the current pixel andits 4 neighbors, and divide it through 5, or thus fill in 5 timesthe value 0.2 in the filter:With such a small filter matrix, this gives only a very softblur:
With a bigger filter you can blur it a bit more (don't forget tochange the filterWidth and filterHeight values):
The sum of all elements of the filter should be 1, but instead offilling in some floating point value inside the filter, instead thefactor is divided through the sum of all elements, which is13.
This blurs it a bit more already:
Mv3 Filter Picsart Image
The more blur you want, the bigger the filter has to be, or you canapply the same small blur filter multiple times.
If your kernel is an entire box filled with the same value (with appropriate scaling factorso all elements sum to 1.0), then the blur is called a box blur. If you want a very largebox blur, then the naive convolution code in this tutorial is too slow. But you can implementit easily with a much faster algorithm: Since every value has the same factor, you can goloop through the pixels of the image line by line, and sum N values (with N the width of thebox) and divide through the appropriate scaling factor. Then for every next pixel, you add thevalue of the next pixel that appears in the box, and subtract the value from the leftmost pixelthat disappears from the box. After doing this for each scanline horizontally, do the samevertically (to optimize use of the CPU cache, when doing it vertically, ensure that you stillimplement it in such way that you still operate in scanline order in practice rather than percolumns, so store a sum per column). This all requires care with how you treat the edges (wherethe box is partially out of bounds) and the case where the image is smaller than the box. Nocode is given here as it goes a bit beyond the scope of this tutorial.
Gaussian Blur
In the blurring above, the kernel we used is rather harsh. A much smoother bluris achieved with a gaussian kernel. With a gaussian kernel, the value exponentiallydecreases as we go away from the center. The formula is: G(x) = exp(-x * x / 2 * sigma * sigma) / sqrt(2 * pi * sigma * sigma)For 2D, you apply this formula in the X direction, then in the Y direction (it is separable), or combined it gives:G(x, y) = exp(-(x * x + y * y) / (2 * sigma * sigma)) / (2 * pi * sigma * sigma)
In the formulas:
*) sigma determines the radius (in theory the radius is in finite, but in practice due to the exponential backoffthere is a point where it becomes too small to see, the larger sigma is the further away this is)*) x and y must be coordinates such that 0 is in the center of the kernel
The above formula tells hwo to make arbitrarily large kernels. However, here are simple examples that can beused immediately:
Approximation to 3x3 kernel:
#define filterWidth 3#define filterHeight 3double filter[filterHeight][filterWidth] ={ 1, 2, 1, 2, 4, 2, 1, 2, 1,};double factor = 1.0 / 16.0;double bias = 0.0;
Approximation to 5x5 kernel:
#define filterWidth 5#define filterHeight 5double filter[filterHeight][filterWidth] ={ 1, 4, 6, 4, 1, 4, 16, 24, 16, 4, 6, 24, 36, 24, 6, 4, 16, 24, 16, 4, 1, 4, 6, 4, 1,};double factor = 1.0 / 256.0;double bias = 0.0;
An exact intead of approximate example:
#define filterWidth 3#define filterHeight 3double filter[filterHeight][filterWidth] ={ 0.077847, 0.123317, 0.077847, 0.123317, 0.195346, 0.123317, 0.077847, 0.123317, 0.077847,};double factor = 1.0;double bias = 0.0;
For larger radius (such as you can try in a painting program that has gaussianblur), you need larger kernels. The naive convolution implementationlike used in this tutorial would become too slow in practice for large radiusgaussian blurs. But there are solutions to that: using the Fourier Transform asdescribed in the Fourier Transform tutorial of this series, or an even faster approximation:The fast approximation involves doing multiple box blurs in a row, three in a rowapproximated it already very well. How to do a fast box blur is explained in theprevious chapter. The reason this works is that a gaussian distribution arisesnaturally from a sum of other processes.
Motion Blur
Motion blur is achieved by blurring in only 1 direction. Here's a9x9 motion blur filter:It's as if the camera is moving from the top left to the bottomright, hence the name.
FindEdges
A filter to find the horizontal edges can look like this:Mv3 Filter Picsart Download
A filter of 5x5 instead of 3x3 was chosen, because the result of a3x3 filter is too dark on the current image. Note that the sum ofall the elements is 0 now, which will result in a very dark imagewhere only the edges it detected are colored.The reason why this filter can find horizontal edges, is that theconvolution operation with this filter can be seen as a sort ofdiscrete version of the derivative: you take the current pixel andsubtract the value of the previous one from it, so you get a valuethat represents the difference between those two or the slope ofthe function.
Here's a filter that'll find vertical edges instead, and uses bothpixel values below and above the current pixel:
Here's yet another possible filter, one that's good at findingedges of 45°. The values '-2' were chosen for no particularreason at all, just make sure the sum of the values is 0.
And here's a simple edge detection filter that detects edges in alldirections:
Sharpen
To sharpen the image is very similar to finding edges, add theoriginal image, and the image after the edge detection to eachother, and the result will be a new image where the edges areenhanced, making it look sharper. Adding those two images is doneby taking the edge detection filter from the previous example, andincrementing the center value of it with 1. Now the sum of thefilter elements is 1 and the result will be an image with the samebrightness as the original, but sharper.Here's a more subtle sharpen filter:
Here's a filter that shows the edges excessively:
Emboss
An emboss filter gives a 3D shadow effect to the image, the resultis very useful for a bumpmap of the image. It can be achieved bytaking a pixel on one side of the center, and subtracting one ofthe other side from it. Pixels can get either a positive or anegative result. To use the negative pixels as shadow and positiveones as light, for a bumpmap, a bias of 128 is added to the image.Now, most parts of the image will be gray, and the sides will beeither dark gray/black or bright gray/white.For example here's an emboss filter with an angle of 45°:
If you really want to use it as bumpmap, grayscale it:
Here's a much more exaggerated emboss filter:
Mean and Median Filter
Both the Mean Filter and the Median Filter can be used to remove noise from an image.A Mean Filter is a filter that takes the average of the current pixel and its neighbors,for example if you use its 8 neighbors it becomes the filter with kernel:This is an ordinary blur filter. We can test it on the following image with so called 'Salt and Pepper' Noise:
When applied, it gives a blurry result:
The Median Filter does somewhat the same, but, instead of taking the mean or average, it takes the median.The median is gotten by sorting all the values from low to high, and then taking the value in the center. If thereare two values in the center, the average of these two is taken. A median filter gives better results to remove salt and pepper noise,because it completely eliminates the the noise. With an average filter, the color value of the noise particles are still usedin the average calculations, when taking the median you only keep the color value of one or two healthy pixels. The median filteralso reduces the image quality however.
Such a median filter can't be done with a convolution,and a sorting algorithm is needed, in this case combsort was chosen, which is a relatively fast sorting algorithm.
To get the median of the current pixel and its 8 neighbors, set filterWidth and filterHeight to 3, but you can also make it higherto remove larger noise particles.
The arrays red, green and blue will contain the values of the current pixel and all of its neighbors,and these are the arrays that'll be sorted by the sorting algorithm to be able to take the median value.The main function applies the filter, calculates the medians and then draws the result.
The array contains the value of every color in the rectangular area you're working on, but it's not sorted so you can't immediatly take the median of it.Sorting it and taking the center element is one way, but it's in theory faster to use a selection algorithm to select the k-th largest element, with k = size / 2.This is implemented below with a very simple selection algorithm. It's possible to use the standard C++ function nth_element instead, which would be simpler and faster, but we're implementingall algorithms ourselves in this tutorial. Note that, unlike the statistical definition of median this will not takethe average of two elements in case of even array but just take one of them.
Here's again the noisy image:
The 3x3 median filter removes its noise:
Higher sizes of filters go pretty slow, because the code is very unoptimized. More specialized, much faster algorithms for 2D median filter exists but that's beyond the scope of this tutorial.The results of higher sizes are somewhat artistic, so here is the result of different sizes:
5x5:
9x9:
15x15:
Side note: The median algorithm implementation above is very slow. Whether using C++'s nth_elementfunction or the toy 'selectKth' here, both provide little benefit for a median of 9 or 25 numbers. Nomatter what the theoretical complexity of the algorithm on large N is, if you only operate on a certainsmall finite sized input, you need to take what works best for that input size.
If you want to implement, say, median with 3x3, then you get the fastest solution by using a hardcodedsorting network of size 9 of which you take the middle output to get the median. Then apply this for every outputpixel to the corresponding 9 input pixels, and this all per color channel. The advantage of hardcodedis that the algorithm does not need to contain conditionals that depend on the size of the input (conditionals,like ifs and the for loop conditions, are very slow for CPUs as they interrupt the pipeline). No code isprovided here since an efficiant practical implementation is beyond the scope of this tutorial.If interested, look up sorting network, it is a hardcoded series of swaps of two numbers depending on whichis the maximum, find one that is proven to be optimal for the desired input size. Then since we don't want tofully sort but only take the median element, you can leave out every swap that does not contribute to themiddle element output, and replace any swap of which only oneoutput contributes to the middle output element with either min or max. That will give the theoreticallyfastest possible implementation, speedups on top of that can only be to make use of parallelismand/or better CPU instructions.
Conclusion
This article contained code to apply convolution filters on images,and showed a few different filters and their result. These are onlythe very basics of image filtering, with bigger filters and a lotof tweaking you can get much better filters.The Fourier Transform article shows a different way to filterimages, in the frequency domain. There Low Pass, High Pass and BandPass filters are discussed.
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