Backprojection networks have achieved promising super-resolution performance for nature images but not well be explored in the remote sensing image super-resolution (RSISR) field due to the high computation costs. In this paper, we propose a Scale-aware Backprojection Transformer termed SPT for RSISR. SPT incorporates the backprojection learning strategy into a Transformer framework. It consists of Scale-aware Backprojection-based Self-attenion Layers (SPALs) for scale-aware low-resolution feature learning and Scale-aware Backprojection-based Transformer Blocks (SPTBs) for hierarchical feature learning. A backprojection-based reconstruction module (PRM) is also introduced to enhance the hierarchical features for image reconstruction. SPT stands out by efficiently learning low-resolution features without excessive modules for high-resolution processing, resulting in lower computational resources. Experiment results on UCMerced and AID datasets demonstrate that SPT obtains state-of-the-art results compared to other leading RSISR methods.

Nondestructive inspection technology based on machine vision can effectively improve the efficiency of fresh fruit quality inspection. However, fruits with smooth skin and less texture are easily affected by specular highlights during the image acquisition, resulting in light spots appearing on the surface of fruits, which severely affects the subsequent quality inspection. Aiming at this issue, we propose a new specular highlight removal algorithm based on multi-band polarization imaging. First of all, we realize real-time image acquisition by designing a new multi-band polarization imager, which can acquire all the spectral and polarization information through single image capture. Then we propose a joint multi-band-polarization characteristic vector constraint to realize the detection of specular highlight, and next we put forward a Max-Min multi-band-polarization differencing scheme combined with an ergodic least-squares separation for specular highlight removal, and finally, the chromaticity consistency regularization is used to compensate the missing details. Experimental results demonstrate that the proposed algorithm can effectively and stably remove the specular highlight and provide more accurate information for subsequent fruit quality inspection. Besides, the comparison of algorithm speed further shows that our proposed algorithm has a good tradeoff between accuracy and complexity.

Due to the influence of imaging equipment or environment, a hyperspectral image (HSI) is often unavoidably blurred in the acquisition process, which results in the spatial and spectral information loss of the HSI. The existing HSI deblurring methods can address the problem, however, they neglect the intrinsic structured sparsity and thus reduce the deblurring performance. Aiming at this issue, we propose a new HSI deblurring method based on transformed structured sparsity with smoothness. We first use the local piecewise smoothness to obtain the spatial and spectral sparsity of an HSI in the gradient domain. Then, to capture the refined sparsity, we exploit the transform sparsity learning framework to encode the structured sparsity self-adaptively in transform space, where the sparse structures of transformed operators can be depicted by Laplacian scale mixture, i.e., the sparsity can be expressed as the product of a hidden positive scalar multiplier and a Laplacian vector. The visual and quantitative comparisons of experimental results on three HSI datasets indicate that our method outperforms state-of-the-arts.