This paper is the first of a series of papers in which we will apply the methods we have developed for high-precision astrometry (and photometry) with the Hubble Space Telescope (HS T ) to the case of wide-field ground-based images. In particular, we adapt the software originally developed for WFPC2 to ground-based, wide field images from the WFI at the ESO 2.2 m telescope. In this paper, we describe in details the new software, we characterize the WFI geometric distortion, discuss the adopted local transformation approach for proper-motion measurements, and apply the new technique to two-epoch archive data of the two closest Galactic globular clusters: NGC 6121 (M 4) and NGC 6397. The results of this exercise are more than encouraging. We find that we can achieve a precision of ∼7 mas (in each coordinate) in a single exposure for a well-exposed star, which allows a very good cluster-field separation in both M 4, and NGC 6397, with a temporal baseline of only 2.8, and 3.1 years, respectively.

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Convolutional neural networks (CNNs) are deep neural networks that can be trained on large databases and show outstanding performance on object classification, segmentation, image denoising etc. In the past few years, several image denoising techniques have been developed to improve the quality of an image. The CNN based image denoising models have shown improvement in denoising performance as compared to non‐CNN methods like block‐matching and three‐dimensional (3D) filtering, contemporary wavelet and Markov random field approaches etc. which had remained state‐of‐the‐art for years. This study provides a comprehensive study of state‐of‐the‐art image denoising methods using CNN. The literature associated with different CNNs used for image restoration like residual learning based models (DnCNN‐S, DnCNN‐B, IDCNN), non‐locality reinforced (NN3D), fast and flexible network (FFDNet), deep shrinkage CNN (SCNN), a model for mixed noise reduction, denoising prior driven network (PDNN) are reviewed. DnCNN‐S and PDNN remove Gaussian noise of fixed level, whereas DnCNN‐B, IDCNN, NN3D and SCNN are used for blind Gaussian denoising. FFDNet is used for spatially variant Gaussian noise. The performance of these CNN models is analysed on BSD‐68 and Set‐12 datasets. PDNN shows the best result in terms of PSNR for both BSD‐68 and Set‐12 datasets.

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