White noise

Gene

Quantum mechanics

Algorithm

Gaussian

Pixel

Additive white Gaussian noise

Robustness (evolution)

Median filter

Gaussian noise

Image processing

Telecommunications

Biochemistry

Computer science

Impulse noise

Physics

Sparse approximation

Impulse (physics)

Gradient noise

Pattern recognition (psychology)

Mathematics

Image (mathematics)

Noise floor

Salt-and-pepper noise

Artificial intelligence

Geometry

Noise measurement

Chemistry

Value noise

Artificial neural network

Noise reduction

Noise (video)

Convolutional neural network

Reduction (mathematics)

Speech recognition

Signal Processing Image Communication

The removal of mixed-noise is an ill-posed problem due to high level of non-linearity in the distribution of noise. Most commonly encountered mixed-noise is the combination of additive white Gaussian noise (AWGN) and impulse noise (IN) that have contrasting characteristics. A number of methods from the cascade of IN and AWGN reduction to the state-of-the-art sparse representation have been reported to reduce this common form of mixed-noise. In this paper, a new learning-based algorithm using the convolutional neural network (CNN) model is proposed to reduce the mixed Gaussian-impulse noise from images. The proposed CNN model adopts computationally efficient transfer learning approach to obtain an end-to-end map from noisy image to noise-free image. The model has a small structure yet it is capable of providing performance superior to that of the well established methods. Experimental results on different settings of mixed-noise show that the proposed CNN-based denoising method performs significantly better than the sparse representation and patch-based methods do both in terms of accuracy and robustness. Moreover, due to the lightweight structure, the denoising operation of the proposed CNN-based method is computationally faster than that of the previously reported methods.

Mixed Gaussian-impulse noise reduction from images using convolutional neural network