A computer-implemented method of machine learning, for learning a Convolutional Neural Network architecture for estimating a degradation generated by a denoiser on a ray traced image, the method comprising: - providing a dataset obtained by: • providing pairs of images, each pair comprising a reference image and a respective denoised image; • for each pair of images: ∘ providing the pair of images to a pre-trained CNN architecture similar to the CNN architecture being learnt for estimating a degradation generated by a denoiser on a ray traced image; ∘ computing a difference between a first normalized feature of the denoised image and a second normalized feature of the reference image, the first and second normalized features being the output of a same layer of the pre-trained CNN architecture; ∘ computing an error map representing the computed difference; and ∘ adding the respective denoised image, the error map to the dataset and a respective noisy image of the reference image, the noisy image being the one from which the denoised image has been obtained; - learning the CNN architecture based on the provided dataset, the learning comprising taking as input an image generated by the denoiser and its corresponding noisy image of the dataset, and outputting an error map. - providing a dataset obtained by: • providing pairs of images, each pair comprising a reference image and a respective denoised image; • for each pair of images: ∘ providing the pair of images to a pre-trained CNN architecture similar to the CNN architecture being learnt for estimating a degradation generated by a denoiser on a ray traced image; ∘ computing a difference between a first normalized feature of the denoised image and a second normalized feature of the reference image, the first and second normalized features being the output of a same layer of the pre-trained CNN architecture; ∘ computing an error map representing the computed difference; and ∘ adding the respective denoised image, the error map to the dataset and a respective noisy image of the reference image, the noisy image being the one from which the denoised image has been obtained; • providing pairs of images, each pair comprising a reference image and a respective denoised image; • for each pair of images: ∘ providing the pair of images to a pre-trained CNN architecture similar to the CNN architecture being learnt for estimating a degradation generated by a denoiser on a ray traced image; ∘ computing a difference between a first normalized feature of the denoised image and a second normalized feature of the reference image, the first and second normalized features being the output of a same layer of the pre-trained CNN architecture; ∘ computing an error map representing the computed difference; and ∘ adding the respective denoised image, the error map to the dataset and a respective noisy image of the reference image, the noisy image being the one from which the denoised image has been obtained; ∘ providing the pair of images to a pre-trained CNN architecture similar to the CNN architecture being learnt for estimating a degradation generated by a denoiser on a ray traced image; ∘ computing a difference between a first normalized feature of the denoised image and a second normalized feature of the reference image, the first and second normalized features being the output of a same layer of the pre-trained CNN architecture; ∘ computing an error map representing the computed difference; and ∘ adding the respective denoised image, the error map to the dataset and a respective noisy image of the reference image, the noisy image being the one from which the denoised image has been obtained; - learning the CNN architecture based on the provided dataset, the learning comprising taking as input an image generated by the denoiser and its corresponding noisy image of the dataset, and outputting an error map.