Comparison gallery of image scaling algorithms

An image size can be changed in several ways. Consider resizing a 160x160 pixel photo to the following 40x40 pixel thumbnail and then scaling the thumbnail to a 160x160 pixel image. Also consider doubling the size of the following image containing text.

Low-resolution images

Thumbnail	Text

Comparison of scaling methods

Original photo	Upscaled thumbnail	Upscaled text	Algorithm and description
			Nearest-neighbor interpolation One of the simpler ways of increasing the size, replacing every pixel with a number of pixels of the same color. The resulting image is larger than the original, and preserves all the original detail, but has (possibly undesirable) jaggedness. The diagonal lines of the "W", for example, now show the "stairway" shape characteristic of nearest-neighbor interpolation. Other scaling methods below are better at preserving smooth contours in the image.
			Bilinear interpolation Linear (or bilinear, in two dimensions) interpolation is typically good for changing the size of an image, but causes some undesirable softening of details and can still be somewhat jagged.
			Bicubic interpolation Better scaling methods include bicubic interpolation, Lanczos resampling and Mitchell-Netravali filters.
			Fourier-based interpolation Simple Fourier based interpolation based on padding of the frequency domain with zero components (a smooth-window-based approach would reduce the ringing). Beside the good conservation of details, notable is the ringing and the circular bleeding of content from the left border to right border (and way around).
			Edge-directed interpolation Edge-directed interpolation algorithms aim to preserve edges in the image after scaling, unlike other algorithms which can produce staircase artifacts around diagonal lines or curves. Examples of algorithms for this task include New Edge-Directed Interpolation (NEDI),[1][2] Edge-Guided Image Interpolation (EGGI),[3] Iterative Curvature-Based Interpolation (ICBI),[4] and Directional Cubic Convolution Interpolation (DCCI).[5] A study found that DCCI had the best scores in PSNR and SSIM on a series of test images.[6]
			Pixel art scaling algorithms For magnifying computer graphics with low resolution and few colors (usually from 2 to 256 colors), better results will be achieved by pixel art scaling algorithms such as hqx. These produce sharp edges and maintain high level of detail.
			Image tracing Vectorization first creates a resolution-independent vector representation of the graphic to be scaled. Then the resolution-independent version is rendered as a raster image at the desired resolution. This technique is used by Adobe Illustrator Live Trace, Inkscape, and several recent papers.[7] Scalable Vector Graphics are well suited to simple geometric images, while photographs do not fare well with vectorization due to their complexity.
			Deep convolutional neural networks Using machine learning, convincing details are generated as best guesses by learning common patterns from a training data set. The upscaled result is sometimes described as a hallucination because the information introduced may not correspond to the content of the source. Enhanced deep residual network (EDSR) methods have been developed by optimizing conventional residual neural network architecture.[8] Programs that use this method include waifu2x, Imglarger and Neural Enhance.
			Deep convolutional neural networks using perceptual loss Developed on the basis of the super-resolution generative adversarial network (SRGAN) method,[9] enhanced SRGAN (ESRGAN)[10] is an incremental tweaking of the same generative adversarial network basis. Both methods rely on a perceptual loss function[11] to evaluate training iterations.