# Interpolation-Filtering Method for Image Improvement in Digital Holography  
[PERSON]  
Laser Physics Department, Institute for Laser and Plasma Technologies, National Research Nuclear University MEPHI (Moscow Engineering Physics Institute), Kashirskoe Shosse 31, 115409 Moscow, Russia; alexkozlov@yandex. entire field under consideration without scanning. This allows for reconstructing images of all layers of the medium volume with high resolution in real-time, which is important in biology [16; 17; 18; 19] and medicine [20; 21]. Despite the unique capabilities of the digital holographic registration method, there is a set of factors limiting the accuracy of the results. The limiting factors are related to both the registration system and the principle of hologram recording [22]: speckle noise, twin image, zero-order [23], and camera noise. The widespread use of digital cameras as radiation recorders has contributed to the development and improvement of various technologies [24]. The recording speed, resolution, and dynamic range of cameras are improving every year. These parameters are given in the device data sheets. But for areas where the essential parameter is the maximum accuracy of the obtained data [25], the noise of digital cameras is of great importance, which is often missing in the data sheets of even scientific cameras [26]. Digital holography also belongs to such a field where dynamic intensity changes during the time of hologram registration lead to distortions in the region of the reconstructed image. Therefore, it is necessary to select a digital camera for holographic applications based on all its characteristics, including noise and radiometric characteristics [22]. Many approaches [27] have been developed to suppress the mentioned negative factors in digital holography. They can be grouped into 2 main groups: optical [28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41] and digital [42; 43; 44; 45; 46; 47; 48; 49] solutions. Both approaches are applied in practice and have their own advantages and disadvantages. A widely used optical solution involves the registration of a large number of holograms with different parameters and their subsequent averaging [28; 29; 30; 31; 32; 33; 34; 35; 36]. Diffusers [28; 29; 30], sensor [31] and object [32] shifting, different wavelengths [33], polarization directions [34], angles [35], shifts [36], etc., are used to change the parameters. Although such approaches allow the achieve a sufficiently high quality of the reconstructed image, the hardware complications required for their realization have an impact on the size and cost of the holographic setup. In addition, the registration of many holograms with different parameters requires substantial time expenditures, which complicates the application of such approaches in problems where the observation of reconstructed images in quasi-real time is required. To reduce the influence of speckles, incoherent radiation is also used [38; 39; 40]. This approach provides a significant improvement in the quality of the reconstruction. However, the scene depth is usually reduced [32]. Alternatives to optical approaches include digital (image processing) filtering methods [42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53]. A large number of digital methods have been developed based on different principles: simple mathematical operations (finding the mean [50]; mean [42], median [43], or mean-median [44] filters), weighted local (Lee and Frost filters [46]) and non-local means (Non-local means--NLM [47]) averaging using as weights coefficients depending on the distance between pixels and the differences of signals in them, 3D transforms (Block matching 3D filter--BM3D [48]) and deep learning [54; 55; 56]. Such methods have high efficiency, do not require hardware complications, and are realized significantly faster. Neural network methods require training on large datasets, presumably for each application problem.