Images from the MIT-Adobe 5K dataset

First column shows the input images.

Second column shows the retouched images by Expert C.

Third column shows the predicted results for HDRNet.

Fourth and fifth column shows the results for 3 dimensions using PCA and VAE respectively.

Finally we show the results using our method in the last column.

You can toggle between the predicted and the target image by clicking on the images. Moreover, you can rearrange the images in increasing or decreasing order of the metrics.

ID	Input	Expert C	HDRNet	PSNR 21.925	FLIP 0.330	Delta E 2000 8.361	PCA_3_dimensions	PSNR 19.036	FLIP 0.377	Delta E 2000 10.151	Var_AutoEncoder_3_latents	PSNR 24.435	FLIP 0.187	Delta E 2000 4.626	Ours	PSNR 38.693	FLIP 0.097	Delta E 2000 1.662
a4011				22.397	0.278	7.031		20.978	0.344	8.385		20.162	0.267	6.549		39.788	0.082	1.529
a4041				23.186	0.275	5.375		17.941	0.421	9.054		24.908	0.171	3.259		32.321	0.129	1.771
a4071				26.432	0.241	5.486		17.976	0.457	12.122		15.117	0.353	10.680		35.734	0.113	2.532
a4101				13.534	0.517	17.177		22.152	0.273	6.586		26.574	0.135	2.764		39.299	0.070	1.063
a4131				22.143	0.305	5.890		16.829	0.457	10.821		20.775	0.246	5.413		33.805	0.130	1.722
a4161				16.720	0.428	10.430		25.184	0.272	6.086		24.830	0.156	3.491		37.953	0.084	1.190
a4191				16.618	0.449	12.597		19.611	0.366	9.193		21.869	0.218	4.827		36.210	0.100	1.701
a4221				18.597	0.450	10.988		23.589	0.277	6.408		28.159	0.141	3.731		42.002	0.071	1.163
a4251				13.865	0.599	17.165		16.005	0.502	14.402		27.637	0.122	3.625		28.118	0.202	3.738
a4281				22.210	0.292	5.178		11.547	0.592	16.756		22.821	0.204	4.576		38.332	0.099	1.509
a4311				28.548	0.173	4.757		26.433	0.180	4.837		30.844	0.095	2.826		49.125	0.039	0.827
a4341				21.970	0.314	8.460		19.550	0.338	9.165		22.023	0.222	5.113		35.371	0.105	1.798
a4371				24.262	0.305	5.548		25.637	0.232	5.844		23.459	0.207	3.992		43.205	0.061	1.180
a4401				23.102	0.220	6.320		21.536	0.234	7.053		24.853	0.143	4.917		48.678	0.038	0.919
a4431				20.992	0.422	6.813		11.581	0.555	16.539		24.450	0.169	3.263		35.583	0.124	1.677
a4461				25.085	0.245	5.354		20.886	0.295	7.125		29.445	0.120	3.171		50.754	0.046	1.208
a4491				26.508	0.198	4.519		20.990	0.317	7.430		26.110	0.151	4.003		35.464	0.106	1.681
a4521				25.782	0.286	5.204		15.524	0.484	11.577		22.907	0.239	5.055		33.086	0.138	2.214
a4551				28.996	0.200	4.873		14.991	0.505	14.626		27.471	0.142	3.883		29.735	0.174	3.046
a4581				25.457	0.224	5.060		14.785	0.489	12.091		23.981	0.194	4.469		32.936	0.116	1.953
a4611				24.583	0.237	6.131		21.208	0.327	7.769		28.221	0.151	3.529		36.190	0.099	1.598
a4641				14.002	0.608	19.014		25.611	0.222	5.489		29.403	0.101	2.374		41.990	0.058	0.864
a4671				21.301	0.351	8.991		13.051	0.477	13.896		19.351	0.280	6.694		46.752	0.047	1.058
a4701				19.984	0.337	9.470		26.391	0.183	5.122		29.535	0.127	4.049		53.219	0.051	1.293
a4731				21.859	0.310	7.262		18.301	0.379	13.034		29.000	0.121	3.043		33.608	0.131	2.268
a4761				31.238	0.144	3.548		14.870	0.530	15.253		27.133	0.140	3.624		29.749	0.171	3.017
a4791				14.885	0.560	16.805		11.425	0.633	18.989		20.477	0.314	6.550		35.839	0.125	1.763
a4821				24.656	0.231	6.684		17.477	0.388	10.761		23.947	0.181	5.514		42.189	0.078	1.526
a4851				23.909	0.303	5.594		19.580	0.350	10.032		24.960	0.162	4.115		37.731	0.091	1.309
a4881				16.846	0.397	10.364		16.738	0.424	11.962		18.380	0.311	7.381		33.920	0.124	1.919
a4911				27.256	0.206	5.073		24.166	0.248	5.912		25.161	0.179	4.421		46.979	0.047	0.911
a4941				14.670	0.452	14.397		16.599	0.303	10.528		17.967	0.237	7.132		42.500	0.050	1.242
a4971				26.361	0.198	4.328		24.900	0.201	4.701		23.928	0.185	4.057		52.870	0.037	0.747