Abstract: A multi-scale image processing algorithm for enhancing contrast in an electronic representation of an image represented by a) decomposing said digital image into a set of detail images at multiple resolution levels and a residual image at a resolution level lower than said multiple resolution levels, b) processing at least one pixel of said detail images, c) computing a processed image by applying a reconstruction algorithm to the residual image and the processed detail images, said reconstruction algorithm being such that if it were applied to the residual image and the detail images without processing, then said digital image or a close approximation thereof would be obtained.

Abstract: Tomographic images acquired by iterative reconstruction of low quality projection images, are enhanced by the steps of correcting at an iteration step the result of the previous iteration step by means of a back-projection of the result of a comparison of a projection image and the forward projection of the result of the previous iteration step whereby this result is enhanced by subjecting it to a trained neural network.

Abstract: A processing method for enhancing the image quality of an image, more particularly a digital medical grey scale image, that comprises the steps of a) decomposing an original image into multiple detail images at different resolution levels and/or orientations, b) processing the detail images to obtain processed detail images, c) computing a result image by applying a reconstruction algorithm to the processed detail ages, said reconstruction algorithm being such that if it were applied to the detail images without processing, then said original image or a close approximation thereof would be obtained, the processing of the detail images comprises the steps of: d) calculating at least one conjugate detail image, and e) computing at least one value of the processed detail images as a function of said conjugate detail image and said detail images.

Abstract: This invention is related to a multi-scale image processing algorithm for enhancing contrast to noise ratio in an electronic representation of an image represented by a) decomposing said digital image into a set of detail images at multiple resolution levels and a residual image at a resolution level lower than said multiple resolution levels, b) processing said detail images, c) computing a processed image by applying a reconstruction algorithm to the residual image and the processed detail images, said reconstruction algorithm being such that if it were applied to the residual image and the detail images without processing, then said digital image or a close approximation thereof would be obtained. The processing comprises the steps of: d) calculating a local variance value for at least one detail image pixel within a neighbourhood of said detail image pixel; e) enhancing or suppressing at least one detail image pixel as a function of said local variance value and said detail image pixel value.

Abstract: This invention is related to a method for enhancing the contrast and other image quality aspects of an electronic representation of an image that is based on a multi-scale decomposition and recomposition method, wherein the image enhancement steps involve the processing of the detail images by a conversion function, which is optimized by the algorithm itself by means of optimizing the defining parameters of this conversion function by a cost-function based optimization.

Abstract: A multi-scale image processing algorithm for enhancing contrast in an electronic representation of an image represented by a) decomposing said digital image into a set of detail images at multiple resolution levels and a residual image at a resolution level lower than said multiple resolution levels, b) processing at least one pixel of said detail images, c) computing a processed image by applying a reconstruction algorithm to the residual image and the processed detail images, said reconstruction algorithm being such that if it were applied to the residual image and the detail images without processing, then said digital image or a close approximation thereof would be obtained.

Abstract: Digital signal representations of sub-images are obtained by applying an optimization process wherein a sum is minimized, the sum having a first term representing a measure of the consistency of the sum of a digital representations of sub-images with said radiographic image and wherein the second term is a sum of cost functions each describing the type of one of said sub-images.

Abstract: Tomographic images acquired by iterative reconstruction of low quality projection images, are enhanced by the steps of correcting at an iteration step the result of the previous iteration step by means of a back-projection of the result of a comparison of a projection image and the forward projection of the result of the previous iteration step whereby this result is enhanced by subjecting it to a trained neural network.

Abstract: Tomographic images, acquired by iterative reconstruction of lower quality images, are enhanced by a trained neural network. Next, the enhanced tomographic images are input to the next step of the iterative reconstruction. For this purpose, one or several neural networks are trained with a first set of tomographic images and a second set of tomographic images at lower quality. The second set of tomographic images at lower quality are acquired by applying an iterative reconstruction algorithm to lower quality projection images. The iterative reconstruction can use a normal quality tomographic image as input.