ABSTRACT In this paper, a hybrid model is developed by integrating a non-linear regression model and optimization-driven deep learner for video super resolution. Initially, the low-resolution frames are subjected to framing, and each frame is provided to both Fractional-Group Search Optimizer-based Deep Belief Network (FrGSO-DBN) classifier and the nonlinear regression model. Then, the output frames of both methods are averaged by the proposed hybrid model and the enhanced video frame is generated. Here, the FrGSO is developed by modifying the update process of the GSO using the fractional theory, to train the DBN such that the weights in the DBN are selected optimally. Finally, the simulation results reveal that the proposed hybrid model achieves high values of 0.8697, 25.637 dB, 0.9171, and 49.821 dB, for the metrics Structural Similarity Index Measure, Peak Signal to Noise Ratio, Feature Similarity Index Measure, and Second Derivative like Measurement of Enhancement, respectively.

中文翻译：

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使用非线性回归和深度学习的视频超分辨率

摘要在本文中，通过集成非线性回归模型和优化驱动的深度学习器来开发视频超分辨率的混合模型。最初，对低分辨率帧进行分帧，并将每一帧提供给基于分数组搜索优化器的深度信念网络 (FrGSO-DBN) 分类器和非线性回归模型。然后，两种方法的输出帧均通过所提出的混合模型进行平均，并生成增强视频帧。在这里，FrGSO 是通过使用分数理论修改 GSO 的更新过程来开发的，以训练 DBN，从而优化选择 DBN 中的权重。最后，仿真结果表明，所提出的混合模型达到了 0.8697、25.637 dB、0.9171 和 49.821 dB 的高值，