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Pixel values histograms are presented for the same aligned movie from the EMPIAR entry 10,288: (A) Cryosparc; (B) FlexAlign; (C) Motioncor2; (D) Relion MotionCor; (E) Warp. CV stands for coefficients of variation (standard deviation as a percentage of the mean). For representation, we removed the outliers by means of the interquartile range (IQR of 80%) method. Figure 9. Scalability of parallel GPU processing. The plots represent the mean processing time in seconds (y-axis) required to process a single movie on one GPU. The x-axis represents the number of GPUs, which is increased in parallel with the number of movies to process. This figure demonstrates the scalability of the algorithm with GPU parallel processing. The scalability analysis was performed on three different movie sizes commonly encountered in single-particle analysis (SPA) experiments. These movie sizes are as follows: (A) corresponds to the 4096 × 4096 × 70 experiment, which represents a movie size typically observed in lower-resolution SPA experiments; (B) represents the 7676 × 7420 × 70 experiment; (C) corresponds to the 11,520 × 8184 × 70 experiment, which is a movie size commonly used in super-resolution acquisitions. To ensure reliable results, each algorithm was executed 10 times per movie size, thereby avoiding unstable runs and obtaining more accurate measurements. Figure 9. Scalability of parallel GPU processing. The plots represent the mean processing time in seconds (y-axis) required to process a single movie on one GPU. The x-axis represents the number of GPUs, which is increased in parallel with the number of movies to process. This figure demonstrates the scalability of the algorithm with GPU parallel processing. The scalability analysis was performed on three different movie sizes commonly encountered in single-particle analysis (SPA) experiments. These movie sizes are as follows: (A) corresponds to the 4096 × 4096 × 70 experiment, which represents a movie size typically observed in lower-resolution SPA experiments; (B) represents the 7676 × 7420 × 70 experiment; (C) corresponds to the 11,520 × 8184 × 70 experiment, which is a movie size commonly used in super-resolution acquisitions. To ensure reliable results, each algorithm was executed 10 times per movie size, thereby avoiding unstable runs and obtaining more accurate measurements. Table 1. Comparison of various movie alignment programs. Table 1. Comparison of various movie alignment programs. ProgramHWMethod + InterpolationCryoSPARCGPUProprietary codeFlexAlignGPUCC + cubic B-spline in space and timeMotionCor2GPUCC + quadratic (space), cubic (time) polynomialsRelion MotionCorCPUCC + quadratic (space), cubic (time) polynomialsWarpGPUCC + cubic B-spline in space and time Table 2. CTF Resolution limit (Å) comparison. The following table presents the means and standard deviations of the CTF criteria for the CTF estimation using two different methods, Gctf and Xmipp. The data in the table correspond to 30 image samples per EMPIAR entry, divided into three datasets: 10,196, 10,288, and 10,314. Table 2. CTF Resolution limit (Å) comparison. The following table presents the means and standard deviations of the CTF criteria for the CTF estimation using two different methods, Gctf and Xmipp. The data in the table correspond to 30 image samples

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Information learning. Yeung et al. [66] deployed a residual scheme with spatial–angular separable (SAS) convolutions for more computationally efficient feature extraction. Jin et al. [67] proposed an all-to-one strategy in which per-view feature extraction is performed in the first step, and then the features are combined across views to construct an intermediate HR light field. Next, regularization is applied in the enhancement stage to improve the cross-view structural consistency. More recent work by Wang et al. [68] used the concept of deformable convolution to compute residual information for light field SR.Apart from the two main schemes described above, there are several other innovative methods worth mentioning. LFNet [69] is an end-to-end deep learning approach for light field SR which replaced the conventional warping/registration approach for alignment with a multi-scale contextual information extraction scheme. The spatial relation between views is exploited by fusing the contextual data, and a bidirectional recurrent CNN was deployed to super-resolve horizontal and vertical image stacks. Finally, a stacked generalization is used to linearly combine the horizontal and vertical image stacks.Some techniques adopt a hybrid capturing system where a single HR image is recorded using a standard 2D camera, and low-resolution (LR) light field views are recorded by a plenoptic camera. The high-frequency information provided by the HR image is used as a reference and propagated to the adjacent LR views for super-resolution. Zheng et al. [70, 71] deploy a patch matching scheme to find the corresponding patches between an HR reference and LR views and then adopt a view synthesis network to build an HR light field. In another hybrid approach, Jin et al. [72] proposed two parallel pipelines to make intermediate super-resolved light fields. One model is based on deep feature extraction from multiple views, and the other transforms the HR components to LR views