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.