Bilinear interpolation is a commonly used upsampling technique in image processing to scale up an image. To calculate a pixel at coordinate $(x, y)$ in the upsampled output image, the technique maps $(x, y)$ to a corresponding real-valued coordinate $(x', y')$ on the original input image based on the ratio of the input size to the output size. It then identifies the four pixels closest to $(x', y')$ on the input image and calculates the final output pixel value based on these four pixels and their relative distances from $(x', y')$. In deep learning, this technique is frequently used to initialize transposed convolutional layers.