Is Architectural Complexity Always the Answer? A Case Study on SwinIR vs. an Efficient CNN
This work addresses the problem of resource constraints in real-world computer vision applications, showing that simpler models can be nearly as effective as complex ones, making it incremental but practically useful.
The paper tackled the trade-off between performance and efficiency in low-light image restoration by comparing SwinIR (a Transformer model) with a lightweight CNN, finding that the CNN achieved a competitive PSNR of 37.4 dB (vs. 39.03 dB for SwinIR) with much lower computational cost, training in only 10 epochs compared to 132 epochs.
The simultaneous restoration of high-frequency details and suppression of severe noise in low-light imagery presents a significant and persistent challenge in computer vision. While large-scale Transformer models like SwinIR have set the state of the art in performance, their high computational cost can be a barrier for practical applications. This paper investigates the critical trade-off between performance and efficiency by comparing the state-of-the-art SwinIR model against a standard, lightweight Convolutional Neural Network (CNN) on this challenging task. Our experimental results reveal a nuanced but important finding. While the Transformer-based SwinIR model achieves a higher peak performance, with a Peak Signal-to-Noise Ratio (PSNR) of 39.03 dB, the lightweight CNN delivers a surprisingly competitive PSNR of 37.4 dB. Crucially, the CNN reached this performance after converging in only 10 epochs of training, whereas the more complex SwinIR model required 132 epochs. This efficiency is further underscored by the model's size; the CNN is over 55 times smaller than SwinIR. This work demonstrates that a standard CNN can provide a near state-of-the-art result with significantly lower computational overhead, presenting a compelling case for its use in real-world scenarios where resource constraints are a primary concern.