SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Table 1: Quantitative results of different methods on DIV2K5G. Group0 denotes the validation set with satisfactory degradation tasks, and Group1-4 represent the validation sets with unsatisfactory degradation tasks. The ESRGAN trained on the non-blind setting and RDSR trained on the MSE-based setting are marked in gray.
Group0	Group1	Group2	Group3	Group4
PSNR	LPIPS	PSNR	LPIPS	PSNR	LPIPS	PSNR	LPIPS	PSNR	LPIPS
ESRGAN	21.49	0.6166	20.29	0.6697	20.95	0.6530	22.42	0.5940	22.02	0.5837
RDSR	25.00	0.5196	21.18	0.6167	23.06	0.5586	25.00	0.4993	25.58	0.4814
MM-RealSR	23.49	0.4549	19.74	0.5326	21.76	0.4731	23.37	0.4205	23.97	0.3989
DASR	23.87	0.4683	19.97	0.5752	22.31	0.5093	24.00	0.4474	24.85	0.4225
BSRGAN	23.99	0.4549	20.07	0.5799	22.45	0.4961	24.34	0.4388	24.80	0.4210
RealSRGAN	23.88	0.4599	20.10	0.5586	22.12	0.5019	23.85	0.4499	24.48	0.4270
RealSRGAN-TG	23.95	0.4617	21.14	0.5323	23.06	0.4802	24.69	0.4248	25.05	0.4112
RealESRGAN	23.85	0.4325	20.10	0.5355	22.07	0.4701	24.30	0.4147	24.58	0.3970
RealESRGAN-TG	23.99	0.4286	21.10	0.5056	23.15	0.4494	24.62	0.3975	25.03	0.3851
RealSwinIR	23.35	0.4468	19.60	0.5624	21.65	0.4905	23.66	0.4265	24.16	0.4077
RealSwinIR-TG	23.90	0.4168	20.62	0.4925	22.70	0.4377	24.56	0.3815	24.98	0.3670
RealHAT	24.26	0.4084	20.64	0.5022	22.44	0.4468	24.42	0.3918	25.02	0.3734
RealHAT-TG	24.31	0.4110	21.41	0.4932	23.20	0.4395	24.87	0.3841	25.32	0.3674
Figure 4: Qualitative results of different methods. Zoom in for details.
RealESRGAN utilize a more complex high-order degradation. As a result, they achieve better LPIPS for real-SR evaluation. However, the two approaches obtain low PSNR performance due to the great difficulty of optimizing. Notably, we can enhance the performance of the pre-trained real-SR network by fine-tuning it on the identified Task Groups (TG). This improvement is particularly significant in Groups 1-4. For instance, on RealESRGAN-TG, we can observe a maximum boost of 1 dB in PSNR and 0.03 in LPIPS.
Our TGSR demonstrates significant superiority when applied to a large degradation space. It is noticeable that RealESRGAN-TG outperforms RealESRGAN even in Group0. This indicates that our approach enhances real-SR performance across nearly the entire degradation space, not just the specifically addressed Groups 1-4. In addition to the identified task groups, our TGSR can also achieve performance gains on the randomized synthetic test sets DIV2K_random and AIM2019 test set as shown in Tab. 2. Additionally, our method achieves a gain on the real scene test set RealSRset. These results clearly demonstrate that our approach does not compromise the performance of certain degradation tasks to enhance the performance of others.
Table 2: Quantitative results of different methods on the real-world test set. The ESRGAN trained on the non-blind setting and RDSR trained on the MSE-based setting are marked in gray.
DIV2K_random	AIM2019	RealSRset-Nikon	RealSRset-Cano
PSNR	LPIPS	PSNR	LPIPS	PSNR	LPIPS	PSNR	LPIPS
ESRGAN	20.63	0.6345	23.16	0.5500	27.40	0.4132	27.73	0.4054
RDSR	24.61	0.5268	24.44	0.4803	26.39	0.4053	26.93	0.3795
MM-RealSR	23.17	0.4394	23.48	0.3917	23.78	0.3841	24.42	0.3664
DASR	23.52	0.4832	23.76	0.4210	26.68	0.3972	27.68	0.3792
BSRGAN	23.76	0.4622	24.20	0.4000	26.11	0.3900	26.90	0.3648
SwinIR	23.13	0.4432	23.89	0.3870	26.20	0.3616	26.68	0.3469
RealESRGAN	23.54	0.4423	23.89	0.3960	25.62	0.3820	26.06	0.3629
RealSRGAN	23.58	0.4710	23.72	0.4247	24.71	0.4159	25.42	0.3902
RealSRGAN-TG	23.79	0.4705	23.97	0.4174	25.18	0.4018	25.93	0.3759
RealESRGAN	23.54	0.4423	23.89	0.3960	25.62	0.3820	26.06	0.3629
RealESRGAN-TG	23.84	0.4368	24.27	0.3899	26.01	0.3819	26.33	0.3637
RealSwinIR	23.67	0.4216	23.98	0.3804	25.70	0.3700	26.43	0.3506
RealSwinIR-TG	23.74	0.4190	24.10	0.3766	26.36	0.3751	27.18	0.3585
RealHAT	24.04	0.4156	24.19	0.3742	25.88	0.3532	26.56	0.3339
RealHAT-TG	24.21	0.4189	24.41	0.3723	26.12	0.3635	26.69	0.3451
Figure 5: RealSwinIR and RealHAT may unreasonably generate unpleasant artifacts or semantic textures that shouldn't be there. However, our method does not have this problem.
Our TGSR obtains better visual results than other methods. The visual results of different methods are shown in Fig. 4 and Fig. 5. TGSR significantly improves existing methods by removing various degradation artifacts and noticeable unpleasant artifacts. For the first four rows of images with complex degradation, the other methods cannot remove the degradation or generate unacceptable artifacts. In contrast, our TGSR handles the degradation well and produces visually pleasant results. For the image on the last row, our method can generate more realistic results than other methods with clear textures. In the RealSwinIR results of Fig. 5, we can observe the unpleasant artifacts in the flat area of Flower image and the fur of cat image. In the RealHAT results, we also find semantic textures appearing where they shouldn't be. For example, a tree texture appears on the train, and fur-like textures are present in the background of the Wolf image. However, our method can remove these artifacts by fine-tuning the real-SR network on identified task groups.
5.3 Ablation and Analysis
Effectiveness of the performance indicator. 1) In Fig. 6 (a) and (b), we present the PSNR improvement achieved by fine-tuning the real-SR network on individual tasks with lower and higher performance indicators. It can be observed that tasks with higher performance indicators exhibit a significant PSNR improvement (about 0.4-1.4dB), while tasks with lower performance indicators
(a) Lower performance indicator
(b) Higher performance indicator
(c) Visual comparison
(a) Comparison on PSNR.
Figure 7: Performance comparison of RealESRGAN and our TGSR/ TGSR with random grouping on (a) PSNR and (b) LPIPS. The results indicate random grouping achieves limited improvement compared with our proposed task grouping. (c) Visual results demonstrate that random grouping chooses a satisfactory degradation task that is not required for further training, while our task grouping method finds an unsatisfactory degradation task that needs to be further improved.
(b) Comparison on LPIPS.
Figure 6: Performance comparison of real-SR networks that trained on the degradation tasks with (a) lower performance indicators and (b) higher performance indicators. The degradation tasks with a lower performance indicator can be further improved in quantitative and qualitative results.
(c) Visual comparison
show only a small PSNR improvement (about  $0.2\mathrm{dB}$ ). This finding indicates that our proposed performance indicator can effectively distinguish between satisfactory and unsatisfactory tasks for a real-SR model in a large degradation space. Additionally, Fig. 6 (c) illustrates that significant visual improvements can be achieved with the unsatisfactory degradation task. 2) Directly fine-tuning a model for each task incurs a high computational cost (empirically requiring at least 10,000 iterations). However, our performance indicator requires only 100 iterations of fine-tuning. It suggests that utilizing the performance indicator can be 100 times faster than direct fine-tuning for distinguishing the unsatisfactory and satisfactory degradation tasks. This further illustrates the superiority of the proposed performance indicator.
Effectiveness of our task grouping algorithm. We compare the performance of RealESRGAN-TG with our task grouping and random grouping to demonstrate the effectiveness of our method. As shown in Fig. 7 (a) and (b), random grouping can only bring very limited performance gains of about  $0.2\mathrm{dB}$  on PSNR and about 0.01 on LPIPS. In contrast, the groups generated by our task grouping algorithm show a significant improvement up to 1dB on PSNR and 0.03 on LPIPS. These results demonstrate the significance of a well-designed task grouping approach. Furthermore, Fig. 7 (c) shows that random grouping can not select the unsatisfactory task, while our task grouping algorithm can find it.
Study of the performance upper bound. (1) We randomly select 100 single tasks and then add the corresponding small range of similar degradation parameters to each task, named single-task network with range (see details in appendix). Fig. 8 shows that the existence of related tasks can improve the upper bound of some tasks (about  $40\%$ ). However, there has been a performance drop for other tasks due to similar degradation parameters that may not strictly represent similar tasks. (2) To further show the superiority of our TGSR, we fine-tune the RealESRGAN model on each group to obtain
Figure 8: Performance comparison of SR network with a single task and a range of tasks.
Table 3: Ablation experiments on the performance upper bound. We fine-tune RealESRGAN directly on each task group to get the corresponding performance upper bound, denoted as RealESRGAN-SG.
Metrics	Model	Group0	Group1	Group2	Group3	Group4
PSNR (↑)	RealESRGAN	23.85	24.58	24.00	22.07	20.10
RealESRGAN-SG	23.63	24.58	24.39	22.67	20.46
RealESRGAN-TG (ours)	23.88	25.08	24.62	23.11	21.06
LPIPS (↓)	RealESRGAN	0.4325	0.3970	0.4147	0.4701	0.5355
RealESRGAN-SG	0.4242	0.3792	0.3929	0.4478	0.4990
RealESRGAN-TG (ours)	0.4291	0.3845	0.3981	0.4524	0.5087
their empirical performance upper bound for these degradation groups, and the results are denoted as RealESRGAN-SG. As presented in Tab. 3, the fine-tuned models exceed the baseline models and obtain significant performance improvement on LPIPS. Although RealESRGAN-TG cannot surpass the empirical upper bound performance on LPIPS, it still achieves comparable performance. Moreover, our RealESRGAN-TG obtains higher PSNR compared to RealESRGAN-SG. This further shows the superiority of our method and suggests that multi-task learning enhances the performance of the specific task.
Table 4: Quantitative results of different methods on DIV2K8G. Group0 denotes the validation set with satisfactory degradation tasks, and Group1-7 represents the validation sets with unsatisfactory degradation tasks. The ESRGAN trained on the non-blind setting and RDSR trained on the MSE-based setting are marked in gray.
Metrics	ESRGAN	RDSR	BSRGAN	Real-SwinIR	DASR	MM-RealSR	RealESRGAN	RealESRGAN-TG (ours)
Group0	PSNR (↑)	21.39	25.12	24.19	23.61	23.97	23.61	24.08	24.10
LPIPS (↓)	0.6251	0.5181	0.4567	0.4430	0.4697	0.4335	0.4297	0.4262
Group1	PSNR (↑)	19.81	20.99	19.96	19.52	19.77	19.52	19.92	20.97
LPIPS (↓)	0.6812	0.6325	0.6151	0.5954	0.6012	0.5572	0.5584	0.5249
Group2	PSNR (↑)	21.10	22.55	21.91	21.16	21.77	21.39	21.67	22.72
LPIPS (↓)	0.6636	0.5755	0.5091	0.4988	0.5175	0.4814	0.4823	0.4652
Group3	PSNR (↑)	21.02	23.82	23.24	22.66	23.06	22.54	22.94	23.93
LPIPS (↓)	0.6301	0.5225	0.4657	0.4528	0.4795	0.4445	0.4390	0.4188
Group4	PSNR (↑)	22.10	24.64	23.96	23.37	24.05	23.20	23.63	24.41
LPIPS (↓)	0.6221	0.5171	0.4617	0.4429	0.4596	0.4335	0.4294	0.4097
Group5	PSNR (↑)	22.78	25.30	24.64	23.99	24.49	23.61	24.36	25.14
LPIPS (↓)	0.5705	0.4873	0.4248	0.4113	0.4245	0.4072	0.4043	0.3821
Group6	PSNR (↑)	22.42	25.32	24.58	23.87	24.52	23.71	24.38	24.86
LPIPS (↓)	0.5849	0.4917	0.4255	0.4153	0.4269	0.4060	0.4039	0.3889
Group7	PSNR (↑)	22.49	25.37	24.50	23.85	24.43	23.74	24.37	24.76
LPIPS (↓)	0.5868	0.4958	0.4337	0.4227	0.4398	0.4110	0.4083	0.3976
Impact of the number of task groups. In Tab. 4, we group the unsatisfactory tasks into more compact new groups using new thresholds of [0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2], with the corresponding number of degradation tasks being [14, 15, 14, 31, 53, 116, 258]. The results demonstrate that our TGSR method exhibits steady improvement on the unsatisfactory tasks in Groups 1-7, while achieving comparable performance on the satisfactory tasks in Group 0. This indicates that our approach can be applied with a more fine-grained division of unsatisfactory tasks based on user requirements.
We have re-examined the real-SR problem through the lens of multi-task learning and introduced a novel approach, TGSR, to address task competition in real-SR. TGSR aims to identify and enhance the degradation tasks where a real-SR model underperforms. It involves a task grouping method and a simple fine-tuning approach using the identified task groups. Comprehensive evaluation confirms the effectiveness of our approach in producing high-quality super-resolution results in real-world scenarios. One potential limitation of our method is the need for adequate sampling from the degradation space.
This work was supported in part by the National Natural Science Foundation of China under Grant (62276251,62272450), the Joint Lab of CAS-HK, the National Key RD Program of China (NO. 2022ZD0160100), and in part by the Youth Innovation Promotion Association of Chinese Academy of Sciences (No. 2020356).
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7.1 More Details of TGSR
In this section, we provide more training details of our TGSR. As mentioned in the main paper, we follow the RealESRGAN setting to train the real-SR network by standard GAN-based SR loss, including GAN loss  $L_{GAN}$ , L1 loss  $L_{1}$ , and perceptual loss  $L_{per}$ . The total loss can be defined as follows:
$$
\mathcal {L} = w _ {1} \times \mathcal {L} _ {L _ {1}} + w _ {2} \times \mathcal {L} _ {\text {p e r}} + w _ {3} \times \mathcal {L} _ {\mathrm {G A N}}, \tag {6}
$$
The loss weights  $w_{1}, w_{2}$ , and  $w_{3}$  are set to 1, 1, and 0.1, respectively. Specifically, for  $\mathcal{L}_{L_1}$ , we calculate the pixel loss as the  $L_{1}$  distance  $|\hat{y} - y|$ , where  $\hat{y}$  and  $y$  denote the reconstructed HR image and the ground-truth HR image respectively. For the perceptual loss  $\mathcal{L}_{\mathrm{per}}$ , we extract the conv1, conv2, conv3, conv4, conv5 feature maps of  $\hat{y}$  and  $y$  using the pre-trained VGG19 network [36], and the loss is calculated as the weighted sum of the respective  $L_{1}$  distances between the feature maps of  $\hat{y}$  and  $y$ , with weights set to [0.1, 0.1, 1, 1, 1] for each layer. For the adversarial loss  $\mathcal{L}_{\mathrm{GAN}}$ , we use a U-Net discriminator with spectral normalization.
7.2 More Ablation Studies and Additional Experimental Results
7.2.1 Impact of the Increased Task Volume
In the main paper, we conducted experiments with a total of 4,000 (4k) degradation tasks. In this subsection, we have increased the number of tasks to 10,000 (10k) by sampling from the degradation space, in order to investigate the impact of the increased task volume on the results. In Fig. 9, we draw the histograms of the performance indicators computed with 4k (a) and 10k (b) degradation tasks respectively. It can be seen that their distributions are quite similar.
(b)
Figure 9: The histograms of the performance indicators with  $4\mathrm{k}$  (a) and  $10\mathrm{k}$  (b) degradation tasks respectively.
Furthermore, we use the same setup as in the main paper to do task grouping with the 10k degradation tasks and generate the test set for each group. We then compare the performance of TGSR trained with the 10k degradation tasks (TGSR_10k) and the 4k degradation tasks (TGSR_4k). In Tab. 5, we observe that the performance of TGSR_4k on the dataset DIV2K5G_10K is comparable to that of TGSR_10k. This finding suggests that increasing the number of tasks from 4,000 to 10,000 does not lead to significant improvements in performance for TGSR.
7.2.2 Necessity of Iterative Task Grouping
In Tab. 6, we provide the changes in performance improvement scores for various tasks across four training loops in the task grouping process in Alg. 1. It can be observed that many tasks show variations in their performance improvement scores across different training loops. For example, degradation task 5 exhibits a performance improvement of  $0.57\mathrm{dB}$  in the first training loop and  $0.72\mathrm{dB}$  in the third loop. This suggests that certain tasks are given priority during training in each loop, which leads to limited performance improvement for other tasks due to task competition. The observation supports the necessity of iterative task grouping as outlined in Alg. 1.
Table 5: Quantitative results of TGSR trained with the 4k and 10k degradation tasks, respectively. Group0 denotes the validation set with satisfactory degradation tasks, and Group1-4 represents the validation sets with unsatisfactory degradation tasks. The suffix 4k and 10k represent groups generated with the 4k and 10k degradation tasks, respectively.
Metrics	Group0_4k	Group1_4k	Group2_4k	Group3_4k	Group4_4k
RealESRGAN	PSNR (↑)	23.85	20.11	22.08	24.00	24.59
LPIPS (↓)	0.4325	0.5355	0.4701	0.4147	0.3970
RealESRGAN-TG_4k	PSNR (↑)	23.98	21.10	23.15	24.62	25.03
LPIPS (↓)	0.4286	0.5056	0.4494	0.3975	0.3851
RealESRGAN-TG_10k	PSNR (↑)	23.91	21.03	22.98	24.50	24.97
LPIPS (↓)	0.4299	0.5064	0.4525	0.4002	0.3859
Metrics	Group0_10k	Group1_10k	Group2_10k	Group3_10k	Group4_10k
RealESRGAN	PSNR (↑)	23.81	19.85	20.95	23.77	24.38
LPIPS (↓)	0.4324	0.5495	0.5073	0.4237	0.4019
RealESRGAN-TG_4k	PSNR (↑)	24.12	20.90	21.85	24.38	25.03
LPIPS (↓)	0.4272	0.5187	0.4890	0.4046	0.3875
RealESRGAN-TG_10k	PSNR (↑)	24.05	20.94	21.85	24.31	24.93
LPIPS (↓)	0.4285	0.5171	0.4907	0.4052	0.3886
Table 6: Performance improvement score of some degradation tasks across four training loops. NA means that the task is not available, since it has been grouped in the previous loop.
Training loop	Deg.1	Deg.2	Deg.3	Deg.4	Deg.5	Deg.6	Deg.7	Deg.8	Deg.9	Deg.10
Train1	0.45	1.50	0.60	0.66	0.57	0.75	0.55	0.60	0.61	0.46
Train2	0.47	NA	0.63	0.67	0.41	0.76	0.49	0.59	0.64	0.48
Train3	0.48	NA	NA	NA	0.72	NA	0.65	0.63	NA	0.48
Train4	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Degradation	Deg.11	Deg.12	Deg.13	Deg.14	Deg.15	Deg.16	Deg.17	Deg.18	Deg.19	Deg.20
Train1	0.43	0.60	0.47	0.27	0.66	0.53	0.22	0.31	0.63	0.20
Train2	0.42	0.59	0.47	0.33	0.51	0.52	0.18	0.31	0.67	0.21
Train3	0.48	0.63	0.50	0.31	0.73	0.54	0.18	0.37	NA	0.25
Train4	NA	NA	NA	0.35	NA	NA	0.23	0.46	NA	0.26
7.2.3 Comparison with Single-task Network