Computer-implemented method for determining magnetic resonance images showing different contrasts in an examination, wherein magnetic resonance data for all magnetic resonance images are acquired using the same measurement sequence and the same field of view, but with a respectively modified excitation sequence leading to significantly different signal level, noise level and/or signal-to-noise ratio for different contrasts and the magnetic resonance images are reconstructed from their magnetic resonance data sets using at least one reconstruction algorithm, wherein reconstruction comprises at least one denoising step (S2b) that is performed separately from other reconstruction steps (S2a) and uses a denoising algorithm or involves the application of a trained function (39, 39a, 39b, 39c), wherein - after acquisition of the magnetic resonance data, at least one noise strength measure is determined for the magnetic resonance data sets for each contrast, and - denoising strengths for the denoising step (S2b) are chosen individually for each contrast depending on the respective at least one noise strength measure, the method being characterized in that: if the denoising step (S2b) uses a denoising algorithm, a first image result for each contrast is obtained from reconstruction steps (S2a) applied before the denoising step (S2b), wherein in the denoising step (S2b): - the denoising algorithm having a predefined denoising strength is applied to the first image result to determine a second image result, and - the first image result and the second image result are weightedly combined to determine the magnetic resonance image, wherein the weights are chosen dependent on the at least one noise strength measure for the respective contrast, and in that, if the denoising step (S2b) involves the application of a trained function (39, 39a, 39b, 39c), the trained function (39, 39a, 39b, 39c) comprises a variational neural network used to reconstruct magnetic resonance images from k-space magnetic resonance data sets, wherein a data consistency step and a regularization step are repeatedly performed to obtain denoised magnetic resonance images and a denoising strength for each contrast is chosen by adapting the relative scaling between the data consistency step and the regularization step depending on the at least one noise strength measure. - after acquisition of the magnetic resonance data, at least one noise strength measure is determined for the magnetic resonance data sets for each contrast, and - denoising strengths for the denoising step (S2b) are chosen individually for each contrast depending on the respective at least one noise strength measure, if the denoising step (S2b) uses a denoising algorithm, a first image result for each contrast is obtained from reconstruction steps (S2a) applied before the denoising step (S2b), wherein in the denoising step (S2b): - the denoising algorithm having a predefined denoising strength is applied to the first image result to determine a second image result, and - the first image result and the second image result are weightedly combined to determine the magnetic resonance image, wherein the weights are chosen dependent on the at least one noise strength measure for the respective contrast, and in that, - the denoising algorithm having a predefined denoising strength is applied to the first image result to determine a second image result, and - the first image result and the second image result are weightedly combined to determine the magnetic resonance image, wherein the weights are chosen dependent on the at least one noise strength measure for the respective contrast, and in that, if the denoising step (S2b) involves the application of a trained function (39, 39a, 39b, 39c), the trained function (39, 39a, 39b, 39c) comprises a variational neural network used to reconstruct magnetic resonance images from k-space magnetic resonance data sets, wherein a data consistency step and a regularization step are repeatedly performed to obtain denoised magnetic resonance images and a denoising strength for each contrast is chosen by adapting the relative scaling between the data consistency step and the regularization step depending on the at least one noise strength measure.