Update README.md

README.md

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 # DIP-SLR
- Pyorch codes for the paper "Deep sparse and low-rank for hyperspectral denoising", in Proceeding of IGARSS 2022, Kuala Lumpur, July 2022.<br>
+ Offical Python codes for the paper "Deep sparse and low-rank for hyperspectral denoising", in Proceeding of IGARSS 2022, Kuala Lumpur, July 2022.<br>
  **Authors:** Han V. Nguyen $^\ast \dagger$, Magnus O. Ulfarsson , Jakob Sigurdsson $^\ast$ and Johannes R. Sveinsson $^\ast$<br>
  $^\ast$ Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland<br>
 $^\dagger$ Department of Electrical and Electronic Engineering, Nha Trang University, Khanh Hoa, Vietnam<br>
@@ -20,7 +20,7 @@ $^\dagger$ Department of Electrical and Electronic Engineering, Nha Trang Univer
  Spectral and spatial correlation in hyperspectral images (HSIs) can be exploited in HSI processing because it directly induces a sparse and low-rank prior via linear transformations. Researchers have used sparse and low-rank prior as an image prior for HSI restoration, such as denoising, deblurring, and super-resolution. This paper proposes an HSI denoising method that incorporates a sparse and low-rank prior with a deep image prior (DIP). The sparse and low-rank prior is obtained using 2-dimensional discrete wavelet transform (2-D DWT), and singular value decomposition (SVD), while the DIP is provided by the structure of a convolutional neural network (CNN). The combination of a sparse and low-rank prior with a DIP views the CNN-based denoising method similar to a model-based method, inheriting the advantages of both model-based and CNN-based methods. Experimental results with simulated and real HSI datasets show that the proposed method outperforms the conventional sparse and low-rank based methods in both quantitative and qualitative performance.
 
 ## Usage:<br>
-The following folders contanin:
+The following folders contain:
 - data: The simulated (PU and DC) and the real data.
 - models: python scripts define the models
 - utils: additional functions<br>