AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |
Back to Blog
Super resize12/27/2022 ![]() ![]() ![]() It would be better if we could judge the quality of the generated image and reject the ones which are not realistic. Discriminator network and Adversarial Loss:Īs mentioned earlier, PSNR is not a perfect metric to predict if the image is real or not. So, we use deep neural networks(say Vgg19 or Alexnet) as a feature extractor and take the difference in the feature map between the generated image and ground truth image as the loss for this network. Unfortunately, maximizing PSNR alone doesn’t completely work for us as the images generated could be overly smooth which don’t look perceptually real. The higher the PSNR the better the quality of the reconstructed image as it tries to minimize the MSE between the images with respect to the maximum pixel value of the input image. Maxvalue represents the maximum pixel value that exists in the input image(original target image) if the input image is of 8 bit unsigned integer data type then this value will be equal to 255) to the MSE(Mean Squared Error) between the pixel values of the reconstructed image and the original image expressed on logarithmic scale. It can be defined as the ratio between the maximum pixel value(peak signal) of the input image (for e.g. It measures the deviation between the generated high-resolution image to the original image (Natural High-Resolution image). So, we define a metric for quality: Peak Signal to Noise Ratio (PSNR): Hypothetically, if we get this error to zero, that means our network is now able to generate the good quality high-resolution images. ![]() N represents the number of columns of pixels of the image and j represents the index of that column M represents the number of rows of pixels of the image and i represents the index of that row G represents the matrix of the reconstructed high-resolution image The objective of the network is to reduce the mean-squared error(MSE) between the pixels of the generated image and ground-truth image.į represents the matrix of the original image We feed the low-resolution image(let’s say a size of 20×20) to the network and train it to generate the high-resolution image(80×80). Training data is simple: we can collect a large number of high resolution(HR) images off the internet, then down-size them by a factor of 4 that will be our low resolution(LR) images. ![]() So, we will have to use a deconvolution or fractionally strided convolution or a similar layer(sub-pixel convolutional layer) so that the output image size is 4 times the input size. As we already know that the convolution operation always reduces the size of the input. Okay, let’s think about how we would build a convolutional neural network to train a model for increasing the spatial size by a factor of 4. Understanding Deep Learning based Super-resolution: The training model can be used to generate high resolution images with details from low resolution images. Here we use deep learning to learn to predict these values using Generative adversarial networks. When we simply resize images in OpenCV or Scipy, the traditional methods such as “ Interpolation” are used which approximate the values of new pixels based on nearby pixel values which leave much to be desired in terms of visual quality, as the details (e.g. High Resolution(HR) Image: Pixel density within an image is large, hence it offers a lot of details.Ī technique which is used to reconstruct a high-resolution image from one or many low-resolution images by restoring the high-frequency details is called as “Super-Resolution”. Low Resolution(LR) Image: Pixel density within an image is small, hence it offers few details. SUPER RESIZE SOFTWAREImage super-resolution is a software technique which will let us enhance the image spatial resolution with the existing hardware. What if you could use Artificial Intelligence to enhance your photos like those seen on TV? Image super-resolution is the technology which allows you to increase the resolution of your images using deep learning so as to zoom into your images. ![]()
0 Comments
Read More
Leave a Reply. |