The invention discloses a structural tensor total variation-based image and video super-resolution method. The method includes the following steps that: an image degradation model is established, wherein the image degradation model includes a blurring part and a downsampling part and is expressed as an expression described in the descriptions of the invention, wherein x represents an original high-resolution image, B represents a blurring kernel, a symbol, described in the descriptions, represents a convolution process, D represents downsampling of a corresponding multiple, and y represents a generated low-resolution image; a super-resolution model is established, wherein the super-resolution model includes an image interpolation part and a de-blurring part and is expressed as an expression described in the descriptions of the invention, wherein x<^> represents a restored high-resolution image, B represents a blurring kernel, a symbol, described in the descriptions, represents a deconvolution process, D<-1> represents an interpolating process of a corresponding multiple, and y represents a low-resolution image; and a structural tensor total variation regularization constraint-based super-resolution model is constructed and is expressed as an expression described in the descriptions of the invention, wherein Mu is a parameter that can be adjusted, so that the strength of the constraint of a regularization term can be realized. With the structural tensor total variation-based image and video super-resolution method adopted, over-smoothing and an edge step effect caused by a condition that a total variation model directly constrains an image, are avoided, and a processing effect obtained by using the method of the invention more accords with the subjective feeling of eyes.