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Open a photo and get the sharpened, more detailed version of it in a minute! Works like the "Enhance" tool from CSI series at home.
Looks like magic? No, it's just some interesting mathematics.
This technique is called Blind Deconvolution. Of course the theory is not trivial, but not too hard : You can find more information about deconvultion theory and practice on the yuzhikov. Restoration of defocused and blurred images 2. Restoration of defocused and blurred images. Have blurry photos? To implement the platform we adapted and trained existing Mobilenet SSD face models and integrated existing L1 stabilization algorithms. Factors to evaluate.
In order to understand the implications for designing an accurate real-time object stabilization, we evaluated the following factors and set of technologies that might affect the final output:. We scraped images from Widerface, Openimages Human Face category with their metadata.
We used our own images as well collected from our FB profiles. Each model was trained with approx 5, images.
Models training. We obtained the following metrics:.
Real-time implications. Real time video is perceived fluid depending the number of frames shown per second FPS. Bluray standard uses 60 FPS to enhance the experience of high quality video, Youtube uses up to 30 FPS in HD content, but the human eye only requires 20 FPS to perceive the motion as fluid, the minimum number of unique frames required are at least 10 frames duplicated to achieve the 20 FPS required.
In order to show at least 10 frames per second we need to take at most milliseconds per frame to acquire, process and show the image. In our tests with a standard Macbook Pro the time required to capture the image from the webcam and show each HD frame was 30 milliseconds, it means that by only capturing the image from the webcam and showing it, it will show it at 33 FPS.
Check the best 4 anti-shake video editing software we selected for you here. Adobe After Effects 'Warp Stabilizer' works on both the Windows and Mac. Stabilize Shaky Videos with Wondershare Filmora9 [Windows & Mac] . Pick one from the following options: Anti-shake and wobble correction-low, Anti-shake.
It reduces the processing time to a maximum of 70 milliseconds ms — 30 ms. The following chart shows how long in milliseconds it took to each model to detect objects on a frame:.
There are different techniques to stabilize videos, some are feature-based like L1, and use feature extraction algorithms like SURF, in the other hand the non-feature-extraction techniques are typically focused on centering an entire area based on edit actions like crop and resize. We also evaluated tracking algorithms that prevents to perform the object detector over each frame, we used KCF a popular tracking algorithm that over-performed in terms of accuracy. These are the results of the time in milliseconds that each approach took:. Overall results. After comparing the different approaches and to measure the total time in comparison with the minimum time required to achieve real-time performance we obtained the following results:.
Considerations for design.
Only the Mobilenet approach was suitable for real-time image processing. It is important to mention that even that tracking algorithms are faster than apply object detection to each frame, the precision needed for stabilizing a face was poor. That we observe from the results is that machine learning frameworks and proper images for webcams should be used for training the model.
It is not only needed to have an efficient algorithm to achieve real time face objects in general stabilization, it is mandatory to have correct data annotated to have good accuracy and stabilize the sequences properly. Even that tracker algorithms are time effective, the accuracy to track a face is low since the head and shoulders are often included in the stabilized area, making a video that is not really centered in the object stabilized.
Its better to perform object detection on each frame. Sign in. Get started.