When the image enhancement method LLNet (the low-light net) was applied to three-channel images, there're a lot of redundant parameters. To solve this problem, a framework called CAENet (convolutional auto-encoder network) was proposed. Firstly, CAENet combined a low light processing module with a network training module. Secondly, in the encoding and decoding stages, CAENet used a convolutional network to replace the traditional fully connected network. The experimental results show that connecting low-light processing modules with network training can effectively save time costs. At the same time, the use of convolutional networks can reduce network parameters, making network training more efficient, and obtain better low-dimensional representation of images. The experimental results on the Corel5k dataset show that CAENet can effectively improve the image light perception and color perception while reducing network parameters. The experimental results on high-resolution datasets show that CAENet can preserve details for image details without distortion. In addition, for the noisy low-light image, CAENet can enhance the image while achieving the denoising effect, which proves that CAENet has strong robustness.
Fig.3Schematic diagram of convolutional auto-encoder network (CAENet)
Fig.4Image enhancement schematic diagram under manifold learning method
Fig.5Diagram of low light processing stage of LLNet
Fig.6Diagram of full connection of traditional autoencoder
Fig.7Diagram of convolutional process of three-channel image
Fig.8Diagram of convolutional coding and deconvolutional decoding
Fig.9Process of CAENet training
Fig.10Process of change in image dimensions
Fig.11Flow chart of CAENet pseudo code
Fig.12Image feature extraction step by step using CAENet algorithm
Fig.13Effect comparison of different algorithms on Corel5k dataset
Fig.14Original image and low light image in detail enhancement test
Fig.15Comparison of enhancement effects of different algorithms on rock detail in rock low light images
Fig.16Comparison of enhancement effects of different algorithms on reflection of water surface in low-light images of lakes
Fig.17Comparison of enhancement effects of different algorithms on detail of doors and windows in low-light images of buildings
SSIM
HE
CLAHE
MSRCR
AMSRCR
MSRCP
SRIE
LIME
CAENet
岩石细节图像
0.412 4
0.659 6
0.546 8
0.398 9
0.370 7
0.734 5
0.545 1
0.875 9
湖面细节图像
0.804 3
0.689 2
0.701 3
0.725 6
0.600 8
0.775 3
0.700 0
0.827 8
建筑细节图像
0.703 9
0.758 5
0.622 3
0.633 8
0.555 1
0.645 6
0.694 3
0.835 8
Tab.1Comparison of structural similarity index between different image enhancement algorithms and CAENet algorithm
NIQE
HE
CLAHE
MSRCR
AMSRCR
MSRCP
SRIE
LIME
CAENet
岩石细节图像
7.900 6
8.459 4
8.163 6
7.825 8
7.179 6
8.970 7
8.259 4
7.428 4
湖面细节图像
4.687 2
5.135 0
5.317 0
4.610 2
4.665 8
4.151 3
5.714 1
4.526 7
建筑细节图像
5.086 8
5.061 9
5.357 1
5.603 4
5.186 1
4.827 3
5.681 6
3.864 8
Tab.2Comparison of natural image quality evaluator between different image enhancement algorithms and CAENet algorithm
Fig.18Low light noisy images
Fig.19Effect of different algorithms on Gaussian noise
Fig.20Effect of different algorithms on salt and pepper noise
Fig.21Effect of different algorithms on speckle noise
Fig.22Effect of different algorithms on poisson noise
Fig.23Comparison of evaluation indexes by different image enhancement algorithms
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