Method for observing a sample, the sample lying in a sample plane ( P 10) defining radial positions, parameters ( L ( r), α ( r), F w ( r)) of the sample being defined at each radial position ( r), the method comprising: a) illuminating the sample using a light source configured to emit an incident light wave that propagates toward the sample; b) acquiring, using an image sensor, an image ( I 0) of the sample, said image being formed in a detection plane ( P 0), the sample being placed between the light source and the image sensor, the image acquired by the image sensor forming a hologram of the sample; c) from the image acquired by the image sensor, obtaining an image of the sample in the sample plane, the image of the sample in the sample plane corresponding to a distribution of at least one parameter of the sample in the sample plane; the method being characterized in that step c) comprises the following steps: (i) taking into account an initialization image ( A 10 0), in the sample plane; (ii) applying a holographic propagation operator (h) to the initialization image, or to a complex image of the sample resulting from step (v) of a preceding iteration, so as to estimate an estimate ( I ^ 0 n) of the image acquired by the image sensor in the detection plane; (iii) comparing the image ( I 0) acquired in step b) and the image ( I ^ 0 n) estimated in step ii); (iv) depending on the comparison, computing a validity indicator ( ε | F n n), and updating the parameters ( F n ( r), α n ( r), L n ( r)) describing the sample, such that the parameters of the sample are updated to make the validity indicator tend toward a preset value or to minimize the validity indicator; (v) provided that a convergence criterion has not been met or a preset number of iterations has not been reached, updating a complex image ( A 10 n) of the sample, in the sample plane ( P 10), using the parameters updated in step iv), then reiterating steps (ii) to (v); (vi) using the updated sample parameters of the last step (iv) performed to feed a supervised machine learning algorithm, so as to update parameters ( F °(r), L °( r)) of the sample; (vii) using parameters resulting from step (vi) to obtain an update ( A 10 0 °) of the initialization image; (viii) reiterating, at least once, steps (ii) to (v), using, during the first reiteration, the updated initialization image ( A 10 0 °) resulting from step (vii); (ix) obtaining an image of the sample in the sample plane from the updated parameters of step iv) of the last iteration of steps (ii) to (v). a) illuminating the sample using a light source configured to emit an incident light wave that propagates toward the sample; b) acquiring, using an image sensor, an image ( I 0) of the sample, said image being formed in a detection plane ( P 0), the sample being placed between the light source and the image sensor, the image acquired by the image sensor forming a hologram of the sample; c) from the image acquired by the image sensor, obtaining an image of the sample in the sample plane, the image of the sample in the sample plane corresponding to a distribution of at least one parameter of the sample in the sample plane; (i) taking into account an initialization image ( A 10 0), in the sample plane; (ii) applying a holographic propagation operator (h) to the initialization image, or to a complex image of the sample resulting from step (v) of a preceding iteration, so as to estimate an estimate ( I ^ 0 n) of the image acquired by the image sensor in the detection plane; (iii) comparing the image ( I 0) acquired in step b) and the image ( I ^ 0 n) estimated in step ii); (iv) depending on the comparison, computing a validity indicator ( ε | F n n), and updating the parameters ( F n ( r), α n ( r), L n ( r)) describing the sample, such that the parameters of the sample are updated to make the validity indicator tend toward a preset value or to minimize the validity indicator; (v) provided that a convergence criterion has not been met or a preset number of iterations has not been reached, updating a complex image ( A 10 n) of the sample, in the sample plane ( P 10), using the parameters updated in step iv), then reiterating steps (ii) to (v); (vi) using the updated sample parameters of the last step (iv) performed to feed a supervised machine learning algorithm, so as to update parameters ( F °(r), L °( r)) of the sample; (vii) using parameters resulting from step (vi) to obtain an update ( A 10 0 °) of the initialization image; (viii) reiterating, at least once, steps (ii) to (v), using, during the first reiteration, the updated initialization image ( A 10 0 °) resulting from step (vii); (ix) obtaining an image of the sample in the sample plane from the updated parameters of step iv) of the last iteration of steps (ii) to (v).