Revolutionizing hyper spectral image denoising: a squeezenet paradigm

Hyperspectral images (HSIs) frequently experience various types of noise due to atmospheric interference and sensor instability, which impairs the efficiency of subsequent operations. Consequently, HSI denoising has emerged as a crucial component of HSI preprocessing. Conventional approaches often target a single kind of noise and eliminate it repeatedly, which has disadvantages including inefficiency when handling heterogeneous noise. Lately, models based on deep neural networks have shown encouraging results in the general image denoising domain. This study, which aims to overcome shortcomings in previous techniques, provides a novel denoising methodology by leveraging the effectiveness of the SqueezeNet model. For a thorough assessment, the evaluation framework includes four main indicators: PSNR, SSIM, SAM, and ERGAS. The evaluation is based on real-world hyperspectral images from the Harvard Hyperspectral Dataset, which cover a variety of scenarios and illumination circumstances. Fire blocks are used by the SqueezeNet-based denoising model to optimize feature extraction with fewer parameters.Benchmarks for comparison include deep learning technique QRNN3D and classical techniques like ITSReg and BM4D.In order to avoid convergence to suboptimal local minima and to speed up and stabilize the learning process, this work presents an incremental training policy. The suggested SqueezeNet-based HSI denoising model performs exceptionally well, attaining competitive results in terms of PSNR of 34.15, SSIM of 0.92, and SAM of 4.56 in addition to impressive ERGAS of 20.47. This study offers an effective denoising solution for hyperspectral images by addressing shortcomings in current techniques, showcasing improvements in efficiency and accuracy.