Efficient Image Super-Resolution using Large Kernel Convolution Attention Mechanism
Yan Wang · 2024
Super-resolution techniques serve as a cornerstone in the realm of image processing, offering a pathway to elevate the resolution and fidelity of images, thus opening up a myriad of possibilities across various domains within computer vision. In our paper, we introduce an innovative approach aimed at striking a delicate balance between parameter efficiency and performance in the realm of SR tasks. Our method capitalizes on the potent capabilities of the Large Convolution Kernel Attention Mechanism, a novel framework that enables us to achieve remarkable results by harnessing the power of attention mechanisms alongside large convolutional kernels. At the heart of our proposed methodology lies a profound innovation: the seamless integration of attention mechanisms into the SR framework, with a specialized focus on leveraging large convolutional kernels. This strategic fusion allows our model to operate with unparalleled efficiency, dynamically spotlighting crucial features within the image while concurrently dampening the influence of extraneous or less relevant information. This selective attention mechanism proves to be instrumental in driving substantial enhancements in the quality of reconstructed images, effectively refining the fine details and overall perceptual fidelity. The method proposed in this study achieves a balance between the amount of parameters and calculations and super-resolution performance.