An Image Optimization Model Based on NAG-Enhanced Inexact Block Coordinate Descent and Super-Resolution Reconstruction

Abstract

Image processing technology is often combined with deep learning to be applied in various recognition and classification tasks. However, most existing image processing technologies struggle to balance both accuracy and efficiency. To address this issue, this paper proposes an efficient image processing model that embeds Nesterov's accelerated gradient optimization imprecise block coordinate descent algorithm into a super-resolution reconstruction framework. The model first divides the input image into blocks and uses the Mean Shift algorithm to cluster the image blocks to reduce computational complexity. Subsequently, the Nesterov accelerated gradient algorithm was used to accelerate the descent process of the imprecise block coordinate algorithm, and finally the image resolution was improved through super-resolution reconstruction. The experiment is based on the DIV2K dataset, with the alternating direction method of multipliers, stochastic gradient descent, and Super-Resolution Convolutional Neural Network models as comparison baselines, and it is validated in a hardware environment equipped with an AMD Radeon RX 6800 XT GPU. The results show that the average Peak Signal to Noise Ratio and Structural Similarity Index of the proposed algorithm are 33.43dB and 0.916, respectively. The average processing time for a single image is 16ms, and the overall similarity of the images is 90.1%. The minimum image processing time for the proposed image processing model is 36ms, and the processing time for a single image does not exceed 40ms. These results demonstrate that the proposed model not only ensures high accuracy but also meets the efficiency requirements, allowing for effective restoration and optimization of target images. The proposed method offers new insights for image processing and contributes to the optimization of various techniques based on image processing.