Super-resolution (SR) algorithms have now become a bottleneck for several remote sensing applications. SR is a technique that enhances minute details of the image by increasing spatial resolution of imaging systems. SR overcomes the problems of conventional resolution enhancement techniques such as introduction of noise, spectral distortion, and lack of clarity in the details of the image. In this paper, a survey has been conducted since the inception of SR algorithm till the latest state-of-the-art SR techniques to elucidate the importance of the SR algorithms that lead to paradigm shifts in the last two decades revolutionizing toward visually pleasing high-resolution image. Inspired from the natural images, the algorithms addressing the SR problems such as ill-posed, prior and regularization problem, inverse problem, multi-frame problem and illumination and shadow problem in remote sensing applications are analyzed. For an intuitive understanding of the paradigm shifts, publicly available images are tested with representative paradigm shift SR algorithms. The result of this paradigm shift analysis is done both qualitatively and quantitatively in terms of blurs in the image, pattern clarity, edge strength, and super-resolving capability. The convergence of the natural image to the remote sensed image is critically analyzed. The challenges with possible solutions for super-resolving the remote sensed image are recommended. On experimentation, it is found that deep learning-based SR algorithms produces visually pleasing images retaining sharp edges, enhanced spatial data, and clarity in feature representation while zooming at a certain level beyond interest.

中文翻译：

---

遥感应用中超分辨率技术的范式转变

超分辨率 (SR) 算法现在已成为一些遥感应用的瓶颈。SR 是一种通过增加成像系统的空间分辨率来增强图像细节的技术。SR 克服了传统分辨率增强技术的问题，例如引入噪声、光谱失真以及图像细节缺乏清晰度。在本文中，从 SR 算法开始到最新最先进的 SR 技术进行了一项调查，以阐明 SR 算法的重要性，这些算法导致过去二十年范式转变朝着视觉上令人愉悦的高度转变。 - 分辨率图像。受自然图像的启发，该算法解决了不适定、先验和正则化问题、逆问题等SR问题，分析了遥感应用中的多帧问题和光照阴影问题。为了直观地理解范式转换，公开可用的图像使用代表性范式转换 SR 算法进行测试。这种范式转换分析的结果在图像模糊、图案清晰度、边缘强度和超分辨率能力方面进行了定性和定量分析。对自然图像与遥感图像的收敛进行了批判性分析。推荐了超分辨遥感图像的可能解决方案的挑战。在实验中，发现基于深度学习的 SR 算法产生视觉上令人愉悦的图像，保留锐利的边缘、增强的空间数据和特征表示的清晰度，同时在超出兴趣的某个级别进行缩放。