Deepthi K, Shastry Aditya K, Naresh E
Information Science & Engineering, Nitte Meenakshi Institute of Technology, Visvesvaraya Technological University, Bengaluru, India.
Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India.
Sci Rep. 2024 Dec 2;14(1):29853. doi: 10.1038/s41598-024-81163-x.
Hyperspectral remote sensing images obtained from cameras are characterized by high-dimensions and low quality, which makes them unfavorable for various analytics purposes. This is due to the presence of visible and invisible frequencies of the reflected light making it poorly reveal the spectral signatures of the image. Visual communication advancement has paved the need for Image Super-Resolution (SR) which recovers high-resolution images from low-resolution images. Several works were carried out earlier on image SR using variants of supervised and unsupervised models that still lack accuracy. In this paper, we propose an unsupervised learning model titled Gompertz Function-based Convergence War Accelerometric Optimization-GAN framework for generating of High-Resolution (HR) images. The framework comprises a pre-processing stage, where the incoming Low-Resolution (LR) image is preprocessed for noise removal by applying Shannon-Gaussian Filter (S-GF). Following is the Gradient Domain Approach based Tone-Mapping (TM). Skew correction is done to remove distortion and maintain original resolution that may change during TM stage. The next stage comprises the boundary and edge enhancement of the resulting preprocessed image generated by the method of Inverse Gradient Mapping (IGM) followed by patch extraction to extract minute low-frequency information from the resulting boundary and edge-enhanced image. The contrast of the enhanced patches is improved by removing blurriness effect. The preprocessed image patches are then fed into the Gompertz Function-based Convergence War Accelerometric Optimization - GAN for feature mapping on the trained SR Image features that are clustered using Krzanowski and Li- Kantorovich Metric-K-Means clustering Algorithm (KL-KM-KMA) for effective generation of SR image. The developed model is validated for both qualitative and quantitative measurements. Comparisons are made with several other state-of -the-art methods for accuracy of 98.05%, precision of 97.98%, inception score of 8.71, Fréchet Inception Distance of 36.4 with reduced clustering and training time proving the efficiency of the proposed model.
从相机获取的高光谱遥感图像具有高维度和低质量的特点，这使得它们不利于各种分析目的。这是由于反射光中存在可见和不可见频率，导致其难以揭示图像的光谱特征。视觉通信的发展使得图像超分辨率（SR）成为必要，它能从低分辨率图像中恢复高分辨率图像。早期曾使用监督和无监督模型的变体进行图像SR的多项工作，但仍缺乏准确性。在本文中，我们提出了一种无监督学习模型，即基于冈珀茨函数的收敛战争加速度计优化 - 生成对抗网络（GAN）框架，用于生成高分辨率（HR）图像。该框架包括一个预处理阶段，在这个阶段，通过应用香农 - 高斯滤波器（S - GF）对输入的低分辨率（LR）图像进行预处理以去除噪声。接下来是基于梯度域方法的色调映射（TM）。进行倾斜校正以消除失真并保持在TM阶段可能会改变的原始分辨率。下一阶段包括通过逆梯度映射（IGM）方法对所得预处理图像进行边界和边缘增强，然后进行补丁提取，以从所得边界和边缘增强图像中提取微小的低频信息。通过消除模糊效果来改善增强补丁的对比度。然后将预处理后的图像补丁输入到基于冈珀茨函数的收敛战争加速度计优化 - GAN中，对使用克扎诺夫斯基和李 - 康托罗维奇度量 - K均值聚类算法（KL - KM - KMA）聚类的训练后的SR图像特征进行特征映射，以有效生成SR图像。所开发的模型通过定性和定量测量进行了验证。与其他几种先进方法进行了比较，准确率为98.05%，精确率为97.98%，初始得分8.71，弗雷歇初始距离36.4，同时聚类和训练时间减少，证明了所提出模型的有效性。
