Low-Illumination Image Enhancement Algorithm Based on Parallel Residual Network

Qingjiang Chen; Jinyang Li; Qiannan Hu

doi:10.3788/LOP202158.1410015

Journals >Laser & Optoelectronics Progress >Volume 58 >Issue 14 >Page 1410015 > Article

Laser & Optoelectronics Progress
Vol. 58, Issue 14, 1410015 (2021)

Low-Illumination Image Enhancement Algorithm Based on Parallel Residual Network

Qingjiang Chen, Jinyang Li^*, and Qiannan Hu

Author Affiliations

School of Science, Xi’an University of Architecture and Technology, Xi’an, Shaanxi 710055, China

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DOI: 10.3788/LOP202158.1410015 Cite this Article Set citation alerts

Qingjiang Chen, Jinyang Li, Qiannan Hu. Low-Illumination Image Enhancement Algorithm Based on Parallel Residual Network[J]. Laser & Optoelectronics Progress, 2021, 58(14): 1410015 Copy Citation Text

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Fig. 1. Structure of residual learning model

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Fig. 2. Structure of parallel residual network model

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Fig. 3. InceptionNet V1 network structure used in this paper

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Fig. 4. Alternating residual model and local global residual model

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Fig. 5. Comparison of subjective visual results for different combinations of loss functions

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Fig. 6. Comparision of subjective visual results between our algorithm and seven contrast algorithms on real dataset

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Fig. 7. Comparision of subjective visual results between our algorithm and seven contrast algorithms on synthetic dataset

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Fig. 8. Comparison of subjective visual results of different algorithms on low-illumination images without contrast map

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Fig. 9. Three comparative model structures

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Fig. 10. Comparison of subjective visual results of four model structures

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Loss	PSNR	SSIM
L'₁	23.039	0.7947
L'₂	24.833	0.8269
L'₃	27.149	0.8434
L'₄	27.375	0.8807

Table 1. Calculation results of PSNR and SSIM for different combinations of loss functions

Image	Evaluation index	Method
Image	Evaluation index	SSR	MSRCR	Ying	Ren	BIMEF	LIME	Li	Our
Image1	PSNR /dB	12.059	17.844	14.412	11.884	18.412	21.345	20.447	28.331
Image1	SSIM	0.6013	0.5794	0.5969	0.6835	0.6469	0.7898	0.7259	0.8997
Image2	PSNR/dB	13.469	14.007	20.891	14.558	14.829	23.968	22.424	31.087
Image2	SSIM	0.5974	0.5743	0.7559	0.8232	0.6001	0.8559	0.7068	0.9365
Image3	PSNR/dB	18.612	21.041	15.982	10.920	11.706	17.700	21.346	30.112
Image3	SSIM	0.7810	0.7346	0.8321	0.6560	0.6826	0.7209	0.7881	0.9331
Image4	PSNR/dB	15.931	13.706	20.5662	14.780	15.394	24.355	17.858	25.914
Image4	SSIM	0.6581	0.4831	0.6405	0.6504	0.5674	0.8334	0.6849	0.8343

Table 2. PSNR and SSIM of our algorithm and seven contrast algorithms on real dataset

Image	Evaluation index	Method
Image	Evaluation index	SSR	MSRCR	Ying	Li	Ren	BIMEF	LIME	Our
Image1	PSNR /dB	11.841	13.085	19.501	17.415	13.601	13.863	26.417	29.053
Image1	SSIM	0.6656	0.6802	0.7273	0.7778	0.5221	0.7088	0.8363	0.8416
Image2	PSNR /dB	11.253	11.674	22.293	19.002	15.951	16.218	23.830	29.959
Image2	SSIM	0.6031	0.6240	0.7887	0.8049	0.6541	0.7653	0.8323	0.8746
Image3	PSNR /dB	9.8805	10.946	21.450	16.578	17.093	20.704	20.307	26.423
Image3	SSIM	0.5483	0.5690	0.6863	0.6675	0.4814	0.7687	0.7725	0.8073
Image4	PSNR /dB	12.746	13.868	15.871	15.769	11.287	11.613	20.699	26.085
Image4	SSIM	0.5681	0.6006	0.6715	0.7619	0.4996	0.6513	0.8129	0.8318

Table 3. PSNR and SSIM of our algorithm and seven contrast algorithms on synthetic dataset

Method	Evaluation index							NIQE
Method	Image1	Image2	Image3	Average	Image1	Image2	Image3	Average	Image1	Image2	Image3	Average
SSR	6.3635	7.0093	7.0177	6.7968	0.4208	0.6408	0.4511	0.5042	21.543	30.237	28.735	26.838
MSRCR	6.2553	7.1503	7.1858	6.8305	0.4168	0.6437	0.4847	0.4125	21.173	23.282	13.111	19.189
Ying	4.9771	6.4808	6.8622	6.8638	0.2963	0.5007	0.4406	0.5151	9.352	11.769	9.21	10.11
Li	4.7706	5.6065	6.5899	5.6557	0.3614	0.4961	0.3948	0.4174	11.513	12.074	9.927	11.171
Ren	4.2115	5.6318	6.1182	5.3205	0.3559	0.3442	0.2184	0.3062	9.927	17.351	11.738	13.005
BIMEF	5.3003	6.4905	6.8105	6.2004	0.3445	0.6266	0.5277	0.4996	8.793	17.855	13.26	13.303
LIME	5.9361	6.274	7.153	6.4544	0.4394	0.6492	0.3226	0.4704	8.347	20.275	12.72	13.781
Our	6.5744	6.8264	7.0261	6.809	0.3772	0.6425	0.6024	0.5407	8.269	11.727	7.593	9.196

Table 4. Information entropy, NRSS and NIQE of low-illumination images without contrast map

Model	PSNR /dB	SSIM
Model1	29.552	0.9026
Model2	29.564	0.9048
Model3	27.942	0.8208
Model4	26.147	0.8321

Table 5. PSNR and SSIM results of four network models

Image	Model
Image	Model1	Model2	Model3	Model4
Synthetic image	0.6572	0.8374	0.5982	0.6067
Real image	0.6497	0.8679	0.5889	0.6164

Table 6. Comparison of running time of four models for enhancement of single image

Qingjiang Chen, Jinyang Li, Qiannan Hu. Low-Illumination Image Enhancement Algorithm Based on Parallel Residual Network[J]. Laser & Optoelectronics Progress, 2021, 58(14): 1410015

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