  Hyperspectral images (HSIs) are susceptible to various noise factors leading
to the loss of information, and the noise restricts the subsequent HSIs object
detection and classification tasks. In recent years, learning-based methods
have demonstrated their superior strengths in denoising the HSIs.
Unfortunately, most of the methods are manually designed based on the extensive
expertise that is not necessarily available to the users interested. In this
paper, we propose a novel algorithm to automatically build an optimal
Convolutional Neural Network (CNN) to effectively denoise HSIs. Particularly,
the proposed algorithm focuses on the architectures and the initialization of
the connection weights of the CNN. The experiments of the proposed algorithm
have been well-designed and compared against the state-of-the-art peer
competitors, and the experimental results demonstrate the competitive
performance of the proposed algorithm in terms of the different evaluation
metrics, visual assessments, and the computational complexity.
