SlowGuess/NeurIPS_2023_OCR huggingface dataset:
It is commonly believed that a single-task network fine-tuned specifically for a particular degradation task can achieve the upper bound of performance for that task. However, in Fig. 10, we compare the PSNR improvement over RealESRGAN between our TGSR and the single-task network on a set of randomly selected tasks. Interestingly, we observe that the performance of the single-task network even decreases for tasks 1 and 4 in terms of PSNR, while our TGSR, which utilizes task grouping for multi-task learning, consistently improves performance across all tasks. On average, TGSR achieves a PSNR improvement of 0.74dB and an LPIPS improvement of 0.04, whereas the single-task network only achieves a PSNR improvement of 0.30dB and an LPIPS improvement of 0.03. This demonstrates the effectiveness of the task grouping and multi-task learning strategy in TGSR, which leverages the shared information across tasks to improve the generalization ability of the model and achieve better overall performance.
7.2.4 Impact of the Increased Volume of Unsatisfactory Tasks
Tab. 7 presents the results of TGSR with additional unsatisfactory tasks. We categorize the unsatisfied tasks into 9 groups based on thresholds [0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.15, 0.1] (0.15 and 0.1 are used to include more unsatisfactory tasks compared to Tab. 4), with the corresponding number of degradation tasks as [14, 15, 14, 31, 53, 116, 258, 190, 206]. We can observe that TGSR consistently achieves performance improvement on the unsatisfactory tasks in Groups 1-9 while maintaining comparable results on the satisfactory tasks in Group 0. It is worth noting that as the threshold for dividing the groups decreases, the performance gain becomes gradually smaller.
(a) Performance comparison in PSNR.
(b) Performance comparison in LPIPS.
Figure 10: Performance comparison of single-task network and TGSR in (a) PSNR and (b) LPIPS.
Table 7: Quantitative results of different methods on DIV2K10G. Group0 denotes the validation set with satisfactory degradation tasks, and Group1-9 represents the validation sets with unsatisfactory degradation tasks.
Metrics	ESRGAN	RDSR	BSRGAN	RealSwinIR	DASR	MM-RealSR	RealESRGAN	RealESRGAN-TG (ours)
Group0	PSNR (↑)	21.15	24.80	23.66	22.97	23.66	23.28	23.65	23.78
LPIPS (↓)	0.6268	0.5255	0.4616	0.4503	0.4743	0.4403	0.4358	0.4344
Group1	PSNR (↑)	20.01	21.13	20.07	19.62	19.85	19.63	20.01	21.07
LPIPS (↓)	0.6708	0.6267	0.6051	0.5834	0.5923	0.5552	0.5563	0.5188
Group2	PSNR (↑)	21.08	22.38	21.82	20.97	21.53	21.06	21.36	22.47
LPIPS (↓)	0.6687	0.5821	0.5177	0.5092	0.5200	0.4910	0.4922	0.4735
Group3	PSNR (↑)	20.94	23.58	23.06	22.40	22.82	22.30	22.72	23.58
LPIPS (↓)	0.6421	0.5410	0.4809	0.4671	0.4940	0.4564	0.4553	0.4393
Group4	PSNR (↑)	22.10	24.80	24.27	23.56	24.31	23.46	23.82	24.40
LPIPS (↓)	0.6098	0.5082	0.4470	0.4326	0.4496	0.4257	0.4238	0.4047
Group5	PSNR (↑)	22.84	25.58	24.89	24.27	24.91	23.85	24.56	25.07
LPIPS (↓)	0.5626	0.4843	0.4208	0.4057	0.4187	0.4009	0.3974	0.3768
Group6	PSNR (↑)	22.50	25.11	24.39	23.72	24.42	23.64	24.10	24.63
LPIPS (↓)	0.5895	0.4979	0.4400	0.4254	0.4410	0.4161	0.4130	0.3993
Group7	PSNR (↑)	22.41	25.54	24.72	24.03	24.44	23.80	24.50	24.90
LPIPS (↓)	0.5884	0.4892	0.4248	0.4146	0.4341	0.4048	0.4016	0.3897
Group8	PSNR (↑)	20.94	25.08	24.19	23.57	23.77	23.44	24.10	24.52
LPIPS (↓)	0.6223	0.5057	0.4492	0.4354	0.4568	0.4226	0.4211	0.4112
Group9	PSNR (↑)	19.86	24.89	23.85	23.29	23.65	23.44	23.97	24.29
LPIPS (↓)	0.6340	0.5140	0.4624	0.4485	0.4820	0.4333	0.4302	0.4209
7.3 More Visual Results
Fig. 11 presents additional sample images from our validation datasets, including some unsatisfactory degradation tasks from Groups 1-4 and some satisfactory degradation tasks from Group 0. These images are accompanied by the corresponding ground-truth images. Fig. 12 presents an additional qualitative comparison of competing methods on DIV2K5G. Fig. 13 presents an additional qualitative comparison of competing methods on real-world images. Through the grouping and handling of unsatisfactory degradation tasks, our TGSR exhibits enhanced generalization ability when applied to real-world images.
7.3.1 More Sample Images in DIV2K5G
Figure 11: Sample images from different degradation task groups in our DIV2K5G datasets.
7.3.2 More Qualitative Comparisons on DIV2K5G
ESRGAN
RDSR
MM-RealSR
BSRGAN
RealSwinIR
DASR
RealESRGAN
RealESRGAN-TG
Input
RealSwinIR
Figure 12: Qualitative comparison of competing methods on DIV2K5G.
7.3.3 More Qualitative Comparisons on Real-world Images
Input
Figure 13: Qualitative comparison of competing methods on real-world images.