You are provided with a research question, keyphrases about the question, a description of candidate datasets and dataset options. Read the description of popular datasets provided below and select the ones that can be used to validate the following research question. Use your knowledge of machine learning datasets to make the best judgement.
Your response should be formatted as a `json` array. For instance, for the query "Semi supervised image classification", a sample response might be: ["CIFAR-10", "CIFAR-100"]. Do not include any extra text in the response other than the answer array.

Query: We consider the compression artifacts reduction problem, where a compressed image is transformed into an artifact-free

Keyphrases: compression artifacts reduction images

Dataset description:
Set11: Set11 is a dataset of 11 grayscale images. It is a dataset used for image reconstruction and image compression.
MUSDB18: The MUSDB18 is a dataset of 150 full lengths music tracks (~10h duration) of different genres along with their isolated drums, bass, vocals and others stems. The dataset is split into training and test sets with 100 and 50 songs, respectively.
Sentence Compression: Sentence Compression is a dataset where the syntactic trees of the compressions are subtrees of their uncompressed counterparts, and hence where supervised systems which require a structural alignment between the input and output can be successfully trained.
BSDS500: Berkeley Segmentation Data Set 500 (BSDS500) is a standard benchmark for contour detection. This dataset is designed for evaluating natural edge detection that includes not only object contours but also object interior boundaries and background boundaries.
ImageNet: The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.

Options:- A: Set11  B: MUSDB18  C: Sentence Compression  D: BSDS500  E: ImageNet
["BSDS500", "ImageNet"]