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Although remote sensing sensors acquire hyperspectral images (HSI) with outstanding spectral resolution, hardware limitations prevent them from obtaining HSI with superior spatial resolution. Having high-resolution multispectral images (MSI) from the same scene has sparked the concept of HSI-MSI fusion to achieve high-resolution HSI. Recently, deep learning algorithms have shown excellent performance in HSI-MSI fusion. Deep learning algorithms can automatically extract the priors latent in the input data and reconstruct the output image. However, their performance depends on the training data's inherent characteristics. HSI volume is generally significantly larger than MSI volume. While the high-resolution MSI provides richer spatial information, its contribution to fusion is lower due to its smaller volume. To address this volume discrepancy, a deep convolutional neural network named BASFE is proposed. This network employs a two-branch structure that balances the extracted spatio-spectral features from both HSI and MSI input images, enabling effective feature fusion. Therefore, better efficiency can be achieved by using simpler structures. Experiments showed that balancing the data improves the model's performance significantly. Furthermore, to improve efficiency and reduce complexity and computational burden, the spatio-spectral feature extraction residual modules are embedded in a dense architecture to jointly extract these features. Comprehensive experiments on five challenging datasets show that BASFE surpasses state-of-the-art algorithms. Compared to the first- and second-best competing methods, the proposed method achieves an average improvement of 1.58 dB and 3.68 dB in peak signal-to-noise ratio (PSNR) across the five studied datasets. The source code of BASFE is available in https://github.com/rajaei-arash/BASFE_hyperspectral_fusion.git. • A new architecture for fusion is used to balance the priors extracted from images. • It is shown that higher accuracy can be achieved by data balancing. • The SFER modules are embedded in dense structures for effective prior extraction. • This method jointly extracts spatio-spectral features from both HSI and MSI. [ABSTRACT FROM AUTHOR]

Multipliers are one of the most commonly used parts in a system, responsible for performing computations, while significantly contributing to power consumption. In this article, by removing the least significant bits, a new architecture is presented to implement 3 multipliers (Mul-1, Mul-2 and Mul-3), in order to reduce complexity and power consumption. Compared to the previous works, Mul-1 has the most accuracy in addition to its low energy consumption, therefore, it has been able to deliver a good trade-off between accuracy and energy consumption. All proposed designs and existing multipliers have been simulated and compared in 7 nm FinFET technology using Hspice tool. Moreover, the accuracy and quality of the proposed approximate multipliers are also evaluated using MATLAB. The results show that Mul-1 and Mul-3 are very efficient in image processing applications. According to the results, Mul-1 outperforms its counterpart by 10%, 50% and 50% in PDP, NMED and MRED, respectively. Furthermore, Mul-3 has satisfactory MSSIM in DSP applications and is better than its counterpart by 23% and 16% in PDP and MRED. Meanwhile, Mul-2 improves PDP by nearly 53% compared to Mul-1 and has the lowest power consumption. [Display omitted] • To reduce power consumption, some least significant bits are set to zero. • To ensure accuracy in multipliers, compressors have been employed as exact. • The error compensation circuit is presented to enhance precision in the second stage. • The use of multipliers in real applications that have high computational volume. [ABSTRACT FROM AUTHOR]