Abstract: With known image upscaling methods of noisy images, important detail information is lost during denoising. A method for upscaling noisy input images comprises upscaling a noisy input image to obtain a noisy High-Resolution (HR) image, denoising the noisy input image to obtained a denoised Low-Resolution (LR) image, upscaling the denoised LR image to obtain an upscaled denoised LR image, and combining the noisy HR image and the upscaled denoised LR image to generate a denoised HR image.

Abstract: A noise-aware single-image super-resolution (SI-SR) method and apparatus automatically cancels additive noise while adding detail learned from lower scale of an input image. A recent and efficient in-place cross-scale self-similarity prior is exploited for both learning fine detail examples to complement the interpolation-based upscaled image patches and reducing image noise.

Abstract: Image super-resolution (SR) generally enhance the resolution of images. One of SR's main challenge is discovering mappings between low-resolution (LR) and high-resolution (HR) image patches. The invention learns patch upscaling projection matrices from a training set of images. Input images are divided into overlapping patches, which are normalized and transformed to a defined orientation. Different transformations can be recognized and dealt with by using a simple 2D-projection. The transformed patches are clustered, and cluster specific upscaling projection matrices and corresponding cluster centroids determined during training are applied to obtain upscaled patches. The upscaled patches are assembled to an upscaled image.

Abstract: Known super-resolution algorithms are inefficient due to a high degree of redundant calculations, require search operations such as block matching for finding nearest neighbors, and achieve only small magnification factors. An improved method for upscaling an image comprises steps of upscaling the image by pixel interpolation to obtain a coarse upscaled image, and, for each pixel of the coarse upscaled image, determining a nonlinear regression function for a patch of pixels around a current pixel of the coarse upscaled image and enhancing the value of the current pixel by adding the result of the nonlinear regression function, wherein a pixel of an upscaled image is obtained. The nonlinear regression function is obtained from a trained regression tree, based on geometric features of the patch.