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Super-resolution (SR) is a technique to obtain a higher resolution image (frame) by fusing multiple low-resolution (LR) images (frames) of the same scene. In a typical super-resolution algorithm, image registration is one of the most affective steps. The difficulty of this step results in the fact that most of the existing SR algorithms can not cope with local motions because image registration in general assumes global motion. Moreover, modeling SR noise including image registration error has great influence on the performance of the SR algorithms. In this paper, we report that Laplacian distribution assumption is good selection for global and slow motions that can be easily registered, while for fast motion sequences that contain multi-moving objects, Gaussian distribution is better for error modeling. Based on these results, we propose a cost function with weighted L 2 -norm considering the SR noise model where the weights are generated from the error of registration and penalize parts that are inaccurately registered. These weights serve to reject the outlier image regions. Both the objective and subjective results demonstrate that the proposed algorithm gives better results for slow and fast motion sequences.

Jointly enhancing both energy efficiency (EE) and spectrum efficiency (SE) of modulation schemes becomes one of the main issues for 5G mobile communications. Recently, an indexed modulation (IM) technique provides an interesting tradeoff between EE and SE. Data can be conveyed through the combination of subcarriers pattern that can be divided between activated/non-activated subcarriers in the frequency domain. Maximum SE can be attained at half subcarrier activation, hence producing symbols with half energy of the conventional orthogonal frequency-division multiplexing (OFDM) system. In this paper, alternatively, the new concept of sparsely indexing modulation (SIM) on overall subcarrier space is clarified. Sparse (few) subcarrier activations provide much higher EE, while the combinatorial indexing of the sparse subcarriers on the overall subcarriers as a single group spans huge combinatorial space that provides approximately the same SE of the plain OFDM system. The fallacy of indexing difficulty on overall subcarrier space without grouping is resolved. Moreover, a further SE improvement is suggested by introducing permutation-based indexing and combinatorial indexing on over-complete dictionaries. Sparsely indexing represents the cornerstone, which enables compressive sensing tools to enforce IM gains. Based on the conducted simulations, the proposed SIM scheme outperforms the conventional OFDM system in terms of the error performance, the peak-to-average power ratio, and the EE with the same spectral efficiency without channel coding complexity. The proposed SIM scheme is considered one of the energy savingoriented modulations. INDEX TERMS Index modulation, sparse index modulation, OFDM, OFDM-IM, double data/channel sparsity, critical sparsity, combinatorial/permutational indexing, overcomplete/non-orthogonal dictionary indexing, green modulation. The associate editor coordinating the review of this manuscript and approving it for publication was Junaid Shuja. better system processing under compressive sensing (CS) based signal processing approaches. A. GREEN MODULATION Green cellular network relays on the integration of many strategies for minimizing energy at both the base station (BS) and the user equipment (UE) [1], [2]. The growing tendency for employing an energy efficient communication network is accompanied with encountering the unlimited growth in data demands/network capacity [3]. Saving in signal transmission (green radio) represents an essential aspect affecting the overall energy saving. Modulation schemes aims at maximizing both spectral efficiency (SE) and the energy efficiency (EE)

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