A Review on Underwater Image Enhancement Using Deep Residual Framework
DOI:
https://doi.org/10.32628/IJSRST218314Keywords:
Asynchronous training, edge difference loss, residual learning, underwater image enhancementAbstract
There are various factors such as absorption, refraction & the phenomenon of scattering of light by particles suspended in water that are responsible for distorted colors, low contrast & blurred details of original underwater images. The traditional approaches include pre-processing the image using a descattering algorithm. The super-resolution (SR) method is applied. But this method has limitation that major part of the high frequency information is lost during descattering. This paper comes up with a solution for underwater image enhancement using a deep residual framework. Firstly, the generation of synthetic underwater images takes place for which cycle-consistent adversarial networks (CycleGAN) is employed. Further, these synthetic underwater images are used as training data for convolution neural network models. Secondly, the introduction of very-deep super-resolution reconstruction model to underwater resolution applications is carried out. Using this, the underwater Resnet model is proposed. It acts as a residual learning model for underwater image enhancement operations. Furthermore, the training mode & loss function are improved. Then, a multi-term loss function is formed which comprises of proposed edge difference loss & mean squared error loss. An asynchronous training mode is also being proposed that improves the performance of the multi-term loss function. Lastly, the discussion of the impact of batch normalization takes place. After a comparative analysis & underwater image enhancements, we can say that detailed enhancement performance & color correction of these proposed methods are much efficient & superior to that of previous traditional methods & deep learning models.
References
- G. P.Liu, "Underwater Image enhancement using deep residual framework," 2019.
- H.Qi, "Underwater Image enhancement using deep residual framework," 2019.
- C.Zhenge, "Underwater Image enhancement using deep residual framework," 2019.
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- R.Hummel, "Image enhancement by histogram transformation,Compute Graph,Image Process," 2017.
- S.Corchs, "Underwater Image processing. State of the art of restoration and image enhancement methods," 2014.
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