Inferring MAXIM for Text Deblurring: Enhancing Clarity in Image Restoration

The restoration of blurred text images presents unique challenges, as it requires precise reconstruction of character shapes to ensure readability. This study evaluates the Inferring capabilities of the Multi-Axis Multi-Layer Perceptron (MAXIM) model, a versatile architecture for image restoration, on the TextOCR dataset. While MAXIM's pretrained weights, derived from general-purpose deblurring tasks, were able to unblur portions of text, the overall performance—assessed using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) fell short of expectations for text-focused applications. Among the evaluated pre-trained models, the RealBlur_R weights yielded the best results with 22.17 PSNR and 0.707 SSIM. The findings emphasize the limitations of directly applying general-purpose pre-trained models to text-specific deblurring and highlight the need for dataset-specific fine-tuning. This research provides insights into the potential and constraints of MAXIM in addressing the unique challenges of text deblurring tasks and includes a comparative evaluation against the Restormer model.