LatticeNet: Towards Lightweight Image Super-Resolution with Lattice Block

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SwinIR: Image Restoration Using Swin Transformer (2021, methodology): “Method Scale #Params #Mult-Adds Set5 [3] Set14 [87] BSD100 [58] Urban100 [34] Manga109 [60]PSNR SSIM PSNR SSIM PSNR SSIM PSNR SSIM PSNR SSIM CARN [2] ×2 1,592K 222.8G 37.76 0.9590 33.52 0.9166 32.09 0.8978 31.92 0.9256 38.36 0.9765 FALSR-A [12] ×2 1,021K 234.7G 37.82 0.959 33.55 0.9168 32.1 0.8987 3”
Efficient Transformer for Single Image Super-Resolution (2021, methodology): “I, we compare our ESRT with 13 state-of-the-art lightweight SR models, including SRCNN [14], FSRCNN [15], VDSR [16], DRCN [3], LapSRN [43], DRRN [2], MemNet [4], EDSR-baseline [17], SRMDNF [44], CARN [7], FALSR [5], IMDN [11], and LatticeNet [23].”
MambaIR: A Simple Baseline for Image Restoration with State-Space Model (2024, methodology): “Following previous works [47, 91], we also report the number of parameters (#param) and MACs (upscaling a low-resolution image to 1280 × 720 resolution).”
Efficient Long-Range Attention Network for Image Super-resolution (2022, methodology): “We first compare our ELAN-light with state-of-the-art light-weight SR models, including CNN-based models CARN[2], IMDN [18], LAPAR-A[25], Lat-ticeNet[37], and transformer-based models ESRT [36] and SwinIR-light[28].”
Transformer for Single Image Super-Resolution (2021, methodology): “LatticeNet [29] designs the lattice block that simulates the realization of Fast Fourier Transformation with the butterﬂy structure.”