Abstract: An image processing apparatus for forming an enhanced image is disclosed. The apparatus comprises one or more processors configured to: receive one or more input images; form, from each of the one or more input images, a respective feature representation, each feature representation representing features of the respective input image; and subject the one or more feature representations to a symmetric pooling operation to form an enhanced image from at least some of the features of the one or more feature representations identified by the symmetric pooling operation. The apparatus may generate images with increased photoreceptive dynamic range, increased bit depth and signal-to-noise ratio, with less quantization error and richer colour representation.

Abstract: The present disclosure relates to methods and devices for performing depth estimation on image data. In one example, a device performs depth estimation on first and second images captured using one or more cameras having a color filter array. Each, image of the first and second images comprises multiple color channels. Each color channel of the multiple color channels corresponds to a respective color channel of the color filter array. The, device performs the depth estimation by estimating disparity from the color channels of the first and second images.

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: A noise-aware single-image super-resolution (SI-SR) method and apparatus automatically cancels additive noise while adding detail learnt from lower scale of an input image. In contrast to common SI-SR techniques, the input image is not necessarily assumed to be a clean source of examples. Instead, 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. Experimental results show a promising performance, despite of the relatively simple algorithm. Both objective and subjective evaluations show large quality improvements when upscaling images immersed in 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: Traditional image denoising requires image analysis or noise level analysis. Differently, the invention provides denoising by dividing an input image into small square overlapping patches, computing and storing the mean value of each patch, and subtracting it from the patch. Each zero-mean patch is then automatically aligned to a reference orientation by computing a few relevant DCT coefficients of the patch, analyzing the patch orientation in terms of transposition, inversion and horizontal and vertical flipping, and applying a re-orientation transform to automatically pose the patch in a standard orientation, regardless of its contents. The reoriented patches are clustered, and each of the resulting clusters is either shuffled or averaged. Then, all patches are re-transformed back to their original orientations by reversing the previous transforms, their respective mean is added and the denoised image is reconstructed by overlapping the re-transformed and mean added patches.

Abstract: A method for performing super-resolution on an input image having low resolution, comprises generating a generic training data set of descriptors extracted from regions of training images, and for each patch of the input image, determining a defined number of nearest neighbor regions, extracting example patches from the nearest neighbor regions and collecting the example patches in an example patch data base, determining a combination of low-resolution example patches that, according to their descriptors, optimally approximate the current patch, and constructing a high-resolution patch, wherein a super-resolved image is obtained.

Abstract: A method for performing super-resolution on an input image having low resolution, comprises generating a generic training data set of descriptors extracted from regions of training images, and for each patch of the input image, determining a defined number of nearest neighbor regions, extracting example patches from the nearest neighbor regions and collecting the example patches in an example patch data base, determining a combination of low-resolution example patches that, according to their descriptors, optimally approximate the current patch, and constructing a high-resolution patch, wherein a super-resolved image is obtained.